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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 00:59:48 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290473971z9qcs6jv512b8m2.htm/, Retrieved Thu, 28 Mar 2024 11:36:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98824, Retrieved Thu, 28 Mar 2024 11:36:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Mini-Tutorial FMP...] [2010-11-23 00:59:48] [93ab421e12cd1017d2b38fdbcbdb62e0] [Current]
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Dataseries X:
1	25	25	11	11	7	7	8	8	25	23	23
1	17	17	6	6	17	17	8	8	30	25	25
1	18	18	8	8	12	12	9	9	22	19	19
1	16	16	10	10	12	12	7	7	22	29	29
1	20	20	10	10	11	11	4	4	25	25	25
1	16	16	11	11	11	11	11	11	23	21	21
1	18	18	16	16	12	12	7	7	17	22	22
1	17	17	11	11	13	13	7	7	21	25	25
1	30	30	12	12	16	16	10	10	19	18	18
1	23	23	8	8	11	11	10	10	15	22	22
1	18	18	12	12	10	10	8	8	16	15	15
1	21	21	9	9	9	9	9	9	22	20	20
1	31	31	14	14	17	17	11	11	23	20	20
1	27	27	15	15	11	11	9	9	23	21	21
1	21	21	9	9	14	14	13	13	19	21	21
1	16	16	8	8	15	15	9	9	23	24	24
1	20	20	9	9	15	15	6	6	25	24	24
1	17	17	9	9	13	13	6	6	22	23	23
1	25	25	16	16	18	18	16	16	26	24	24
1	26	26	11	11	18	18	5	5	29	18	18
1	25	25	8	8	12	12	7	7	32	25	25
1	17	17	9	9	17	17	9	9	25	21	21
1	32	32	12	12	18	18	12	12	28	22	22
1	22	22	9	9	14	14	9	9	25	23	23
1	17	17	9	9	16	16	5	5	25	23	23
1	20	20	14	14	14	14	10	10	18	24	24
1	29	29	10	10	12	12	8	8	25	23	23
1	23	23	14	14	17	17	7	7	25	21	21
1	20	20	10	10	12	12	8	8	20	28	28
1	11	11	6	6	6	6	4	4	15	16	16
1	26	26	13	13	12	12	8	8	24	29	29
1	22	22	10	10	12	12	8	8	26	27	27
1	14	14	15	15	13	13	8	8	14	16	16
1	19	19	12	12	14	14	7	7	24	28	28
1	20	20	11	11	11	11	8	8	25	25	25
1	28	28	8	8	12	12	7	7	20	22	22
1	19	19	9	9	9	9	7	7	21	23	23
1	30	30	9	9	15	15	9	9	27	26	26
1	29	29	15	15	18	18	11	11	23	23	23
1	26	26	9	9	15	15	6	6	25	25	25
1	23	23	10	10	12	12	8	8	20	21	21
1	21	21	12	12	14	14	9	9	22	24	24
1	28	28	11	11	13	13	6	6	25	22	22
1	23	23	14	14	13	13	10	10	25	27	27
1	18	18	6	6	11	11	8	8	17	26	26
1	20	20	8	8	16	16	10	10	25	24	24
1	21	21	10	10	11	11	5	5	26	24	24
1	28	28	12	12	16	16	14	14	27	22	22
1	10	10	5	5	8	8	6	6	19	24	24
1	22	22	10	10	15	15	6	6	22	20	20
1	31	31	10	10	21	21	12	12	32	26	26
1	29	29	13	13	18	18	12	12	21	21	21
1	22	22	10	10	13	13	8	8	18	19	19
1	23	23	10	10	15	15	10	10	23	21	21
1	20	20	9	9	19	19	10	10	20	16	16
1	18	18	8	8	15	15	10	10	21	22	22
1	25	25	14	14	11	11	5	5	17	15	15
1	21	21	8	8	10	10	7	7	18	17	17
1	24	24	9	9	13	13	10	10	19	15	15
1	25	25	14	14	15	15	11	11	22	21	21
1	13	13	8	8	12	12	7	7	14	19	19
1	28	28	8	8	16	16	12	12	18	24	24
1	25	25	7	7	18	18	11	11	35	17	17
1	9	9	6	6	8	8	11	11	29	23	23
1	16	16	8	8	13	13	5	5	21	24	24
1	19	19	6	6	17	17	8	8	25	14	14
1	29	29	11	11	7	7	4	4	26	22	22
1	14	14	11	11	12	12	7	7	17	16	16
1	22	22	14	14	14	14	11	11	25	19	19
1	15	15	8	8	6	6	6	6	20	25	25
1	15	15	8	8	10	10	4	4	22	24	24
1	20	20	11	11	11	11	8	8	24	26	26
1	18	18	10	10	14	14	9	9	21	26	26
1	33	33	14	14	11	11	8	8	26	25	25
1	22	22	11	11	13	13	11	11	24	18	18
1	16	16	9	9	12	12	8	8	16	21	21
1	16	16	8	8	9	9	4	4	18	23	23
1	18	18	13	13	12	12	6	6	19	20	20
1	18	18	12	12	13	13	9	9	21	13	13
1	22	22	13	13	12	12	13	13	22	15	15
1	30	30	14	14	9	9	9	9	23	14	14
1	30	30	12	12	15	15	10	10	29	22	22
1	24	24	14	14	24	24	20	20	21	10	10
1	21	21	13	13	17	17	11	11	23	22	22
1	29	29	16	16	11	11	6	6	27	24	24
1	31	31	9	9	17	17	9	9	25	19	19
1	20	20	9	9	11	11	7	7	21	20	20
1	16	16	9	9	12	12	9	9	10	13	13
1	22	22	8	8	14	14	10	10	20	20	20
1	20	20	7	7	11	11	9	9	26	22	22
1	28	28	16	16	16	16	8	8	24	24	24
1	38	38	11	11	21	21	7	7	29	29	29
1	22	22	9	9	14	14	6	6	19	12	12
1	20	20	11	11	20	20	13	13	24	20	20
1	17	17	9	9	13	13	6	6	19	21	21
1	22	22	13	13	15	15	10	10	22	22	22
1	31	31	16	16	19	19	16	16	17	20	20
2	24	48	14	28	11	22	12	24	24	26	52
2	18	36	12	24	10	20	8	16	19	23	46
2	23	46	13	26	14	28	12	24	19	24	48
2	15	30	11	22	11	22	8	16	23	22	44
2	12	24	4	8	15	30	4	8	27	28	56
2	15	30	8	16	11	22	8	16	14	12	24
2	20	40	8	16	17	34	7	14	22	24	48
2	34	68	16	32	18	36	11	22	21	20	40
2	31	62	14	28	10	20	8	16	18	23	46
2	19	38	11	22	11	22	8	16	20	28	56
2	21	42	9	18	13	26	9	18	19	24	48
2	22	44	9	18	16	32	9	18	24	23	46
2	24	48	10	20	9	18	6	12	25	29	58
2	32	64	16	32	9	18	6	12	29	26	52
2	33	66	11	22	9	18	6	12	28	22	44
2	13	26	16	32	12	24	5	10	17	22	44
2	25	50	12	24	12	24	7	14	29	23	46
2	29	58	14	28	18	36	10	20	26	30	60
2	18	36	10	20	15	30	8	16	14	17	34
2	20	40	10	20	10	20	8	16	26	23	46
2	15	30	12	24	11	22	8	16	20	25	50
2	33	66	14	28	9	18	6	12	32	24	48
2	26	52	16	32	5	10	4	8	23	24	48
2	18	36	9	18	12	24	8	16	21	24	48
2	28	56	8	16	24	48	20	40	30	20	40
2	17	34	8	16	14	28	6	12	24	22	44
2	12	24	7	14	7	14	4	8	22	28	56
2	17	34	9	18	12	24	9	18	24	25	50
2	21	42	10	20	13	26	6	12	24	24	48
2	18	36	13	26	8	16	9	18	24	24	48
2	10	20	10	20	11	22	5	10	19	23	46
2	29	58	11	22	9	18	5	10	31	30	60
2	31	62	8	16	11	22	8	16	22	24	48
2	19	38	9	18	13	26	8	16	27	21	42
2	9	18	13	26	10	20	6	12	19	25	50
2	13	26	14	28	13	26	6	12	21	25	50
2	19	38	12	24	10	20	8	16	23	29	58
2	21	42	12	24	13	26	8	16	19	22	44
2	23	46	14	28	8	16	5	10	19	27	54
2	21	42	11	22	16	32	7	14	20	24	48
2	15	30	14	28	9	18	8	16	23	29	58
2	19	38	10	20	12	24	7	14	17	21	42
2	26	52	14	28	14	28	8	16	17	24	48
2	16	32	11	22	9	18	5	10	17	23	46
2	19	38	9	18	11	22	10	20	21	27	54
2	31	62	16	32	14	28	9	18	21	25	50
2	19	38	9	18	12	24	7	14	18	21	42
2	15	30	7	14	12	24	6	12	19	21	42
2	23	46	14	28	11	22	10	20	20	29	58
2	17	34	14	28	12	24	6	12	15	21	42
2	21	42	8	16	9	18	11	22	24	20	40
2	17	34	11	22	9	18	6	12	20	19	38
2	25	50	14	28	15	30	9	18	22	24	48
2	20	40	11	22	8	16	4	8	13	13	26
2	19	38	20	40	8	16	7	14	19	25	50
2	20	40	11	22	17	34	8	16	21	23	46
2	17	34	9	18	11	22	5	10	23	26	52
2	21	42	10	20	12	24	8	16	16	23	46
2	26	52	13	26	20	40	10	20	26	22	44
2	17	34	8	16	12	24	9	18	21	24	48
2	21	42	15	30	7	14	5	10	21	24	48
2	28	56	14	28	11	22	8	16	24	24	48




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98824&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98824&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98824&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 8.8810887487407 -1.23162104238467Gender[t] + 0.252442484510157CM[t] + 0.0447956145838131CM_G[t] -0.183344002951501D[t] -0.130645172512862D_G[t] + 0.573211458789485PE[t] -0.284652770114989PE_G[t] -0.144270406337447PC[t] + 0.110306020586238PC_G[t] + 0.207928610225239O[t] + 0.161964789752110O_G[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  8.8810887487407 -1.23162104238467Gender[t] +  0.252442484510157CM[t] +  0.0447956145838131CM_G[t] -0.183344002951501D[t] -0.130645172512862D_G[t] +  0.573211458789485PE[t] -0.284652770114989PE_G[t] -0.144270406337447PC[t] +  0.110306020586238PC_G[t] +  0.207928610225239O[t] +  0.161964789752110O_G[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98824&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  8.8810887487407 -1.23162104238467Gender[t] +  0.252442484510157CM[t] +  0.0447956145838131CM_G[t] -0.183344002951501D[t] -0.130645172512862D_G[t] +  0.573211458789485PE[t] -0.284652770114989PE_G[t] -0.144270406337447PC[t] +  0.110306020586238PC_G[t] +  0.207928610225239O[t] +  0.161964789752110O_G[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98824&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98824&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 8.8810887487407 -1.23162104238467Gender[t] + 0.252442484510157CM[t] + 0.0447956145838131CM_G[t] -0.183344002951501D[t] -0.130645172512862D_G[t] + 0.573211458789485PE[t] -0.284652770114989PE_G[t] -0.144270406337447PC[t] + 0.110306020586238PC_G[t] + 0.207928610225239O[t] + 0.161964789752110O_G[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.88108874874076.8470641.29710.1966410.09832
Gender-1.231621042384674.877395-0.25250.8009950.400497
CM0.2524424845101570.174971.44280.1512110.075605
CM_G0.04479561458381310.1138970.39330.6946670.347334
D-0.1833440029515010.345782-0.53020.5967520.298376
D_G-0.1306451725128620.226144-0.57770.5643450.282172
PE0.5732114587894850.3151161.81910.0709390.035469
PE_G-0.2846527701149890.21516-1.3230.1878960.093948
PC-0.1442704063374470.392223-0.36780.7135310.356766
PC_G0.1103060205862380.2775170.39750.6915940.345797
O0.2079286102252390.2243990.92660.3556520.177826
O_G0.1619647897521100.1595381.01520.3116730.155837

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 8.8810887487407 & 6.847064 & 1.2971 & 0.196641 & 0.09832 \tabularnewline
Gender & -1.23162104238467 & 4.877395 & -0.2525 & 0.800995 & 0.400497 \tabularnewline
CM & 0.252442484510157 & 0.17497 & 1.4428 & 0.151211 & 0.075605 \tabularnewline
CM_G & 0.0447956145838131 & 0.113897 & 0.3933 & 0.694667 & 0.347334 \tabularnewline
D & -0.183344002951501 & 0.345782 & -0.5302 & 0.596752 & 0.298376 \tabularnewline
D_G & -0.130645172512862 & 0.226144 & -0.5777 & 0.564345 & 0.282172 \tabularnewline
PE & 0.573211458789485 & 0.315116 & 1.8191 & 0.070939 & 0.035469 \tabularnewline
PE_G & -0.284652770114989 & 0.21516 & -1.323 & 0.187896 & 0.093948 \tabularnewline
PC & -0.144270406337447 & 0.392223 & -0.3678 & 0.713531 & 0.356766 \tabularnewline
PC_G & 0.110306020586238 & 0.277517 & 0.3975 & 0.691594 & 0.345797 \tabularnewline
O & 0.207928610225239 & 0.224399 & 0.9266 & 0.355652 & 0.177826 \tabularnewline
O_G & 0.161964789752110 & 0.159538 & 1.0152 & 0.311673 & 0.155837 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98824&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]8.8810887487407[/C][C]6.847064[/C][C]1.2971[/C][C]0.196641[/C][C]0.09832[/C][/ROW]
[ROW][C]Gender[/C][C]-1.23162104238467[/C][C]4.877395[/C][C]-0.2525[/C][C]0.800995[/C][C]0.400497[/C][/ROW]
[ROW][C]CM[/C][C]0.252442484510157[/C][C]0.17497[/C][C]1.4428[/C][C]0.151211[/C][C]0.075605[/C][/ROW]
[ROW][C]CM_G[/C][C]0.0447956145838131[/C][C]0.113897[/C][C]0.3933[/C][C]0.694667[/C][C]0.347334[/C][/ROW]
[ROW][C]D[/C][C]-0.183344002951501[/C][C]0.345782[/C][C]-0.5302[/C][C]0.596752[/C][C]0.298376[/C][/ROW]
[ROW][C]D_G[/C][C]-0.130645172512862[/C][C]0.226144[/C][C]-0.5777[/C][C]0.564345[/C][C]0.282172[/C][/ROW]
[ROW][C]PE[/C][C]0.573211458789485[/C][C]0.315116[/C][C]1.8191[/C][C]0.070939[/C][C]0.035469[/C][/ROW]
[ROW][C]PE_G[/C][C]-0.284652770114989[/C][C]0.21516[/C][C]-1.323[/C][C]0.187896[/C][C]0.093948[/C][/ROW]
[ROW][C]PC[/C][C]-0.144270406337447[/C][C]0.392223[/C][C]-0.3678[/C][C]0.713531[/C][C]0.356766[/C][/ROW]
[ROW][C]PC_G[/C][C]0.110306020586238[/C][C]0.277517[/C][C]0.3975[/C][C]0.691594[/C][C]0.345797[/C][/ROW]
[ROW][C]O[/C][C]0.207928610225239[/C][C]0.224399[/C][C]0.9266[/C][C]0.355652[/C][C]0.177826[/C][/ROW]
[ROW][C]O_G[/C][C]0.161964789752110[/C][C]0.159538[/C][C]1.0152[/C][C]0.311673[/C][C]0.155837[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98824&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98824&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.88108874874076.8470641.29710.1966410.09832
Gender-1.231621042384674.877395-0.25250.8009950.400497
CM0.2524424845101570.174971.44280.1512110.075605
CM_G0.04479561458381310.1138970.39330.6946670.347334
D-0.1833440029515010.345782-0.53020.5967520.298376
D_G-0.1306451725128620.226144-0.57770.5643450.282172
PE0.5732114587894850.3151161.81910.0709390.035469
PE_G-0.2846527701149890.21516-1.3230.1878960.093948
PC-0.1442704063374470.392223-0.36780.7135310.356766
PC_G0.1103060205862380.2775170.39750.6915940.345797
O0.2079286102252390.2243990.92660.3556520.177826
O_G0.1619647897521100.1595381.01520.3116730.155837







Multiple Linear Regression - Regression Statistics
Multiple R0.6216762962826
R-squared0.386481417359652
Adjusted R-squared0.340571863556632
F-TEST (value)8.41832223022466
F-TEST (DF numerator)11
F-TEST (DF denominator)147
p-value2.19315676730503e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.42438888696967
Sum Squared Residuals1723.78656963260

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.6216762962826 \tabularnewline
R-squared & 0.386481417359652 \tabularnewline
Adjusted R-squared & 0.340571863556632 \tabularnewline
F-TEST (value) & 8.41832223022466 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 2.19315676730503e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.42438888696967 \tabularnewline
Sum Squared Residuals & 1723.78656963260 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98824&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.6216762962826[/C][/ROW]
[ROW][C]R-squared[/C][C]0.386481417359652[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.340571863556632[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.41832223022466[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]2.19315676730503e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.42438888696967[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1723.78656963260[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98824&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98824&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.6216762962826
R-squared0.386481417359652
Adjusted R-squared0.340571863556632
F-TEST (value)8.41832223022466
F-TEST (DF numerator)11
F-TEST (DF denominator)147
p-value2.19315676730503e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.42438888696967
Sum Squared Residuals1723.78656963260







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12521.88228318778823.11771681221184
23024.69969795905795.30030204094214
32220.67283947823531.32716052176470
42223.2172477003945-1.21724770039453
52522.73996096544022.26003903455985
62319.51969509343213.48030490656795
71719.3385350459548-2.33853504595485
82122.0094817127892-1.00948171278924
91923.7341169344749-4.7341169344749
101522.9461870992115-7.94618709921148
111617.3941561848707-1.39415618487066
122220.75478193400671.24521806599330
132324.3977577855181-1.39775778551813
142321.60128625311071.39871374688932
151922.4316112343517-3.43161123435169
162322.79350634595760.206493654042416
172523.77036272412271.22963727587727
182221.93163764951450.0683623504855226
192623.58466119985342.41533880014656
202923.60609301966845.39390698033157
213225.04079534325966.95920465674042
222522.24419244700412.75580755299586
232826.31735533841881.68264466158119
242523.60449367640521.39550632359481
252522.83127810128922.16872189871082
261821.7760006151216-3.77600061512158
272524.82801820300080.171981796999167
282522.52560393574862.47439606425144
292024.0023423110419-4.00234231104185
301516.5489407322832-1.54894073228325
312425.2136967791899-1.21369677918993
322624.22692510925241.77307489074756
331416.4988057281025-2.49880572810254
342423.68820762411940.31179237588064
352522.29011424697092.70988575302905
362024.8228294406094-4.82282944060945
372121.3379147072532-0.337914707253221
382727.3806373577635-0.38063735776349
392324.8875313004724-1.88753130047237
402525.9236847186639-0.923684718663891
412022.3048028084823-2.30480280848232
422222.7351814508955-0.735181450895486
432524.20338498864210.796615011357932
442523.48883642366101.51116357633896
451723.6354773260822-6.63547732608217
462524.23705304525670.762946954743253
472622.63334127880563.36665872119444
482724.48335679319152.51664320680848
491920.034027614319-1.03402761431901
502222.5712761469369-0.571276146936909
513228.99334525618643.00665474381355
522124.7417584656952-3.74175846569519
531821.5563365981081-3.55633659810815
542323.1025501030034-0.10255010300339
552021.8295927359971-1.82959273599709
562122.6142313584396-1.61423135843962
571719.2372963735279-2.23729637352785
581820.3155783697159-2.31557836971593
591920.9172996003486-1.91729960034864
602222.4071052135827-0.407105213582672
611419.2545777542679-5.25457775426786
621826.5470290665061-8.54702906650608
633523.991131907947311.0088680920527
642918.883085011027310.1169149889727
652122.3522465116134-1.35224651161343
662521.22534675749493.77465324250507
672622.83719972719153.16280027280852
681717.5001681270367-0.500168127036699
692520.48704542767164.51295457232843
702020.3710266060241-0.371026606024123
712221.22329673224720.776703267752821
722422.66000764694831.33999235305170
732123.211232304497-2.211232304497
742625.21224200879950.787757991200536
752420.77056086541283.22943913458719
761620.5381252902889-4.53812529028890
771820.8620827426893-2.86208274268930
781919.5746801581445-0.574680158144457
792117.48608106518823.51391893481175
802218.67641485437513.32358514562487
812319.64061854866653.35938145133348
822924.92513184570984.0748681542902
832120.33238844104750.667611558952548
842322.47915276999750.520847230002503
852723.09334663301993.90665336698007
862525.665739034365-0.665739034365012
872121.1025899837641-0.102589983764137
881017.5450137047189-7.5450137047189
892022.7748382661863-2.7748382661863
902622.40242636314513.59757363685486
912424.1709732057960-0.170973205796026
922932.039524903068-3.03952490306796
931919.637559433908-0.637559433907986
942422.86785351639861.13214648360137
951921.1918508495598-2.19185084955978
962222.2332378774937-0.233237877493683
971724.1770748831824-7.17707488318235
982423.18915257569790.810847424302144
991920.1213719624976-1.12137196249761
1001922.2397545862169-3.2397545862169
1012319.01195289827153.98804710172847
1022723.99969846635343.00030153364658
1031415.0272740449086-1.02727404490862
1042223.0668347665730-1.06683476657305
1052122.480071673226-1.48007167322600
1061823.6785415443543-5.67854154435434
1072023.5712368913594-3.57123689135942
1081923.1012937277056-4.10129372770564
1092422.92318700733251.07681299266752
1102526.0974028906659-1.09740289066594
1112924.57029194303654.42970805696351
1122825.00806463768262.99193536231745
1131715.87959474508431.12040525491573
1142922.44707816052616.55292183947392
1152626.9014120632511-0.901412063251095
1161417.8390211128728-3.83902111287284
1172621.69470808580764.30529191419238
1182020.1628931194827-0.162893119482687
1193224.73787797320987.2621220267902
1202321.28606633760281.71393366239721
1212121.9949450332778-0.994945033277755
1223024.69545439984945.3045456001506
1232420.88895785556723.11104214443277
1242222.6345480739457-0.634548073945693
1252422.26111114416451.73888885583554
1262422.42763447522331.57236552477667
1272420.27712560196593.72287439803414
1281918.04925196308420.95074803691578
1293127.81845366597213.18154633402794
1302226.8821117405066-4.88211174050664
1312720.74531009632676.25468990367333
1321917.50946730120921.49053269879080
1332118.44468556362162.55531443637837
1342323.6545548145521-0.654554814552146
1351920.62733266948-1.62733266948002
1361922.8328678522258-3.83286785222581
1372022.0710595177597-2.07105951775966
1382321.39324534532711.60675465467294
1391720.2204281949549-3.22042819495491
1401722.5158548399329-5.51585483993291
1411719.6490080600547-2.64900806005468
1422124.0813306672545-3.08133066725446
1432123.9449545369319-2.94495453693186
1441820.6650625429321-2.66506254293213
1451920.1098547493404-1.10985474934042
1462024.2900101615384-4.29001016153839
1471517.6814817408554-2.68148174085542
1482421.55555491219712.44444508780294
1492017.93995064964972.06004935035035
1502222.2540686796497-0.254068679649664
1511315.6183134640767-2.61831346407669
1521917.88589379986251.11410620013752
1532121.2774151677469-0.277415167746949
1542322.48369687599430.516303124005703
1551622.0445536366044-6.04455363660442
1562622.07289158947833.92710841052168
1572122.1738873024122-1.17388730241222
1582120.10468558914510.89531441085486
1592423.18820451161000.811795488390044

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 25 & 21.8822831877882 & 3.11771681221184 \tabularnewline
2 & 30 & 24.6996979590579 & 5.30030204094214 \tabularnewline
3 & 22 & 20.6728394782353 & 1.32716052176470 \tabularnewline
4 & 22 & 23.2172477003945 & -1.21724770039453 \tabularnewline
5 & 25 & 22.7399609654402 & 2.26003903455985 \tabularnewline
6 & 23 & 19.5196950934321 & 3.48030490656795 \tabularnewline
7 & 17 & 19.3385350459548 & -2.33853504595485 \tabularnewline
8 & 21 & 22.0094817127892 & -1.00948171278924 \tabularnewline
9 & 19 & 23.7341169344749 & -4.7341169344749 \tabularnewline
10 & 15 & 22.9461870992115 & -7.94618709921148 \tabularnewline
11 & 16 & 17.3941561848707 & -1.39415618487066 \tabularnewline
12 & 22 & 20.7547819340067 & 1.24521806599330 \tabularnewline
13 & 23 & 24.3977577855181 & -1.39775778551813 \tabularnewline
14 & 23 & 21.6012862531107 & 1.39871374688932 \tabularnewline
15 & 19 & 22.4316112343517 & -3.43161123435169 \tabularnewline
16 & 23 & 22.7935063459576 & 0.206493654042416 \tabularnewline
17 & 25 & 23.7703627241227 & 1.22963727587727 \tabularnewline
18 & 22 & 21.9316376495145 & 0.0683623504855226 \tabularnewline
19 & 26 & 23.5846611998534 & 2.41533880014656 \tabularnewline
20 & 29 & 23.6060930196684 & 5.39390698033157 \tabularnewline
21 & 32 & 25.0407953432596 & 6.95920465674042 \tabularnewline
22 & 25 & 22.2441924470041 & 2.75580755299586 \tabularnewline
23 & 28 & 26.3173553384188 & 1.68264466158119 \tabularnewline
24 & 25 & 23.6044936764052 & 1.39550632359481 \tabularnewline
25 & 25 & 22.8312781012892 & 2.16872189871082 \tabularnewline
26 & 18 & 21.7760006151216 & -3.77600061512158 \tabularnewline
27 & 25 & 24.8280182030008 & 0.171981796999167 \tabularnewline
28 & 25 & 22.5256039357486 & 2.47439606425144 \tabularnewline
29 & 20 & 24.0023423110419 & -4.00234231104185 \tabularnewline
30 & 15 & 16.5489407322832 & -1.54894073228325 \tabularnewline
31 & 24 & 25.2136967791899 & -1.21369677918993 \tabularnewline
32 & 26 & 24.2269251092524 & 1.77307489074756 \tabularnewline
33 & 14 & 16.4988057281025 & -2.49880572810254 \tabularnewline
34 & 24 & 23.6882076241194 & 0.31179237588064 \tabularnewline
35 & 25 & 22.2901142469709 & 2.70988575302905 \tabularnewline
36 & 20 & 24.8228294406094 & -4.82282944060945 \tabularnewline
37 & 21 & 21.3379147072532 & -0.337914707253221 \tabularnewline
38 & 27 & 27.3806373577635 & -0.38063735776349 \tabularnewline
39 & 23 & 24.8875313004724 & -1.88753130047237 \tabularnewline
40 & 25 & 25.9236847186639 & -0.923684718663891 \tabularnewline
41 & 20 & 22.3048028084823 & -2.30480280848232 \tabularnewline
42 & 22 & 22.7351814508955 & -0.735181450895486 \tabularnewline
43 & 25 & 24.2033849886421 & 0.796615011357932 \tabularnewline
44 & 25 & 23.4888364236610 & 1.51116357633896 \tabularnewline
45 & 17 & 23.6354773260822 & -6.63547732608217 \tabularnewline
46 & 25 & 24.2370530452567 & 0.762946954743253 \tabularnewline
47 & 26 & 22.6333412788056 & 3.36665872119444 \tabularnewline
48 & 27 & 24.4833567931915 & 2.51664320680848 \tabularnewline
49 & 19 & 20.034027614319 & -1.03402761431901 \tabularnewline
50 & 22 & 22.5712761469369 & -0.571276146936909 \tabularnewline
51 & 32 & 28.9933452561864 & 3.00665474381355 \tabularnewline
52 & 21 & 24.7417584656952 & -3.74175846569519 \tabularnewline
53 & 18 & 21.5563365981081 & -3.55633659810815 \tabularnewline
54 & 23 & 23.1025501030034 & -0.10255010300339 \tabularnewline
55 & 20 & 21.8295927359971 & -1.82959273599709 \tabularnewline
56 & 21 & 22.6142313584396 & -1.61423135843962 \tabularnewline
57 & 17 & 19.2372963735279 & -2.23729637352785 \tabularnewline
58 & 18 & 20.3155783697159 & -2.31557836971593 \tabularnewline
59 & 19 & 20.9172996003486 & -1.91729960034864 \tabularnewline
60 & 22 & 22.4071052135827 & -0.407105213582672 \tabularnewline
61 & 14 & 19.2545777542679 & -5.25457775426786 \tabularnewline
62 & 18 & 26.5470290665061 & -8.54702906650608 \tabularnewline
63 & 35 & 23.9911319079473 & 11.0088680920527 \tabularnewline
64 & 29 & 18.8830850110273 & 10.1169149889727 \tabularnewline
65 & 21 & 22.3522465116134 & -1.35224651161343 \tabularnewline
66 & 25 & 21.2253467574949 & 3.77465324250507 \tabularnewline
67 & 26 & 22.8371997271915 & 3.16280027280852 \tabularnewline
68 & 17 & 17.5001681270367 & -0.500168127036699 \tabularnewline
69 & 25 & 20.4870454276716 & 4.51295457232843 \tabularnewline
70 & 20 & 20.3710266060241 & -0.371026606024123 \tabularnewline
71 & 22 & 21.2232967322472 & 0.776703267752821 \tabularnewline
72 & 24 & 22.6600076469483 & 1.33999235305170 \tabularnewline
73 & 21 & 23.211232304497 & -2.211232304497 \tabularnewline
74 & 26 & 25.2122420087995 & 0.787757991200536 \tabularnewline
75 & 24 & 20.7705608654128 & 3.22943913458719 \tabularnewline
76 & 16 & 20.5381252902889 & -4.53812529028890 \tabularnewline
77 & 18 & 20.8620827426893 & -2.86208274268930 \tabularnewline
78 & 19 & 19.5746801581445 & -0.574680158144457 \tabularnewline
79 & 21 & 17.4860810651882 & 3.51391893481175 \tabularnewline
80 & 22 & 18.6764148543751 & 3.32358514562487 \tabularnewline
81 & 23 & 19.6406185486665 & 3.35938145133348 \tabularnewline
82 & 29 & 24.9251318457098 & 4.0748681542902 \tabularnewline
83 & 21 & 20.3323884410475 & 0.667611558952548 \tabularnewline
84 & 23 & 22.4791527699975 & 0.520847230002503 \tabularnewline
85 & 27 & 23.0933466330199 & 3.90665336698007 \tabularnewline
86 & 25 & 25.665739034365 & -0.665739034365012 \tabularnewline
87 & 21 & 21.1025899837641 & -0.102589983764137 \tabularnewline
88 & 10 & 17.5450137047189 & -7.5450137047189 \tabularnewline
89 & 20 & 22.7748382661863 & -2.7748382661863 \tabularnewline
90 & 26 & 22.4024263631451 & 3.59757363685486 \tabularnewline
91 & 24 & 24.1709732057960 & -0.170973205796026 \tabularnewline
92 & 29 & 32.039524903068 & -3.03952490306796 \tabularnewline
93 & 19 & 19.637559433908 & -0.637559433907986 \tabularnewline
94 & 24 & 22.8678535163986 & 1.13214648360137 \tabularnewline
95 & 19 & 21.1918508495598 & -2.19185084955978 \tabularnewline
96 & 22 & 22.2332378774937 & -0.233237877493683 \tabularnewline
97 & 17 & 24.1770748831824 & -7.17707488318235 \tabularnewline
98 & 24 & 23.1891525756979 & 0.810847424302144 \tabularnewline
99 & 19 & 20.1213719624976 & -1.12137196249761 \tabularnewline
100 & 19 & 22.2397545862169 & -3.2397545862169 \tabularnewline
101 & 23 & 19.0119528982715 & 3.98804710172847 \tabularnewline
102 & 27 & 23.9996984663534 & 3.00030153364658 \tabularnewline
103 & 14 & 15.0272740449086 & -1.02727404490862 \tabularnewline
104 & 22 & 23.0668347665730 & -1.06683476657305 \tabularnewline
105 & 21 & 22.480071673226 & -1.48007167322600 \tabularnewline
106 & 18 & 23.6785415443543 & -5.67854154435434 \tabularnewline
107 & 20 & 23.5712368913594 & -3.57123689135942 \tabularnewline
108 & 19 & 23.1012937277056 & -4.10129372770564 \tabularnewline
109 & 24 & 22.9231870073325 & 1.07681299266752 \tabularnewline
110 & 25 & 26.0974028906659 & -1.09740289066594 \tabularnewline
111 & 29 & 24.5702919430365 & 4.42970805696351 \tabularnewline
112 & 28 & 25.0080646376826 & 2.99193536231745 \tabularnewline
113 & 17 & 15.8795947450843 & 1.12040525491573 \tabularnewline
114 & 29 & 22.4470781605261 & 6.55292183947392 \tabularnewline
115 & 26 & 26.9014120632511 & -0.901412063251095 \tabularnewline
116 & 14 & 17.8390211128728 & -3.83902111287284 \tabularnewline
117 & 26 & 21.6947080858076 & 4.30529191419238 \tabularnewline
118 & 20 & 20.1628931194827 & -0.162893119482687 \tabularnewline
119 & 32 & 24.7378779732098 & 7.2621220267902 \tabularnewline
120 & 23 & 21.2860663376028 & 1.71393366239721 \tabularnewline
121 & 21 & 21.9949450332778 & -0.994945033277755 \tabularnewline
122 & 30 & 24.6954543998494 & 5.3045456001506 \tabularnewline
123 & 24 & 20.8889578555672 & 3.11104214443277 \tabularnewline
124 & 22 & 22.6345480739457 & -0.634548073945693 \tabularnewline
125 & 24 & 22.2611111441645 & 1.73888885583554 \tabularnewline
126 & 24 & 22.4276344752233 & 1.57236552477667 \tabularnewline
127 & 24 & 20.2771256019659 & 3.72287439803414 \tabularnewline
128 & 19 & 18.0492519630842 & 0.95074803691578 \tabularnewline
129 & 31 & 27.8184536659721 & 3.18154633402794 \tabularnewline
130 & 22 & 26.8821117405066 & -4.88211174050664 \tabularnewline
131 & 27 & 20.7453100963267 & 6.25468990367333 \tabularnewline
132 & 19 & 17.5094673012092 & 1.49053269879080 \tabularnewline
133 & 21 & 18.4446855636216 & 2.55531443637837 \tabularnewline
134 & 23 & 23.6545548145521 & -0.654554814552146 \tabularnewline
135 & 19 & 20.62733266948 & -1.62733266948002 \tabularnewline
136 & 19 & 22.8328678522258 & -3.83286785222581 \tabularnewline
137 & 20 & 22.0710595177597 & -2.07105951775966 \tabularnewline
138 & 23 & 21.3932453453271 & 1.60675465467294 \tabularnewline
139 & 17 & 20.2204281949549 & -3.22042819495491 \tabularnewline
140 & 17 & 22.5158548399329 & -5.51585483993291 \tabularnewline
141 & 17 & 19.6490080600547 & -2.64900806005468 \tabularnewline
142 & 21 & 24.0813306672545 & -3.08133066725446 \tabularnewline
143 & 21 & 23.9449545369319 & -2.94495453693186 \tabularnewline
144 & 18 & 20.6650625429321 & -2.66506254293213 \tabularnewline
145 & 19 & 20.1098547493404 & -1.10985474934042 \tabularnewline
146 & 20 & 24.2900101615384 & -4.29001016153839 \tabularnewline
147 & 15 & 17.6814817408554 & -2.68148174085542 \tabularnewline
148 & 24 & 21.5555549121971 & 2.44444508780294 \tabularnewline
149 & 20 & 17.9399506496497 & 2.06004935035035 \tabularnewline
150 & 22 & 22.2540686796497 & -0.254068679649664 \tabularnewline
151 & 13 & 15.6183134640767 & -2.61831346407669 \tabularnewline
152 & 19 & 17.8858937998625 & 1.11410620013752 \tabularnewline
153 & 21 & 21.2774151677469 & -0.277415167746949 \tabularnewline
154 & 23 & 22.4836968759943 & 0.516303124005703 \tabularnewline
155 & 16 & 22.0445536366044 & -6.04455363660442 \tabularnewline
156 & 26 & 22.0728915894783 & 3.92710841052168 \tabularnewline
157 & 21 & 22.1738873024122 & -1.17388730241222 \tabularnewline
158 & 21 & 20.1046855891451 & 0.89531441085486 \tabularnewline
159 & 24 & 23.1882045116100 & 0.811795488390044 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98824&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]25[/C][C]21.8822831877882[/C][C]3.11771681221184[/C][/ROW]
[ROW][C]2[/C][C]30[/C][C]24.6996979590579[/C][C]5.30030204094214[/C][/ROW]
[ROW][C]3[/C][C]22[/C][C]20.6728394782353[/C][C]1.32716052176470[/C][/ROW]
[ROW][C]4[/C][C]22[/C][C]23.2172477003945[/C][C]-1.21724770039453[/C][/ROW]
[ROW][C]5[/C][C]25[/C][C]22.7399609654402[/C][C]2.26003903455985[/C][/ROW]
[ROW][C]6[/C][C]23[/C][C]19.5196950934321[/C][C]3.48030490656795[/C][/ROW]
[ROW][C]7[/C][C]17[/C][C]19.3385350459548[/C][C]-2.33853504595485[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]22.0094817127892[/C][C]-1.00948171278924[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]23.7341169344749[/C][C]-4.7341169344749[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]22.9461870992115[/C][C]-7.94618709921148[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]17.3941561848707[/C][C]-1.39415618487066[/C][/ROW]
[ROW][C]12[/C][C]22[/C][C]20.7547819340067[/C][C]1.24521806599330[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]24.3977577855181[/C][C]-1.39775778551813[/C][/ROW]
[ROW][C]14[/C][C]23[/C][C]21.6012862531107[/C][C]1.39871374688932[/C][/ROW]
[ROW][C]15[/C][C]19[/C][C]22.4316112343517[/C][C]-3.43161123435169[/C][/ROW]
[ROW][C]16[/C][C]23[/C][C]22.7935063459576[/C][C]0.206493654042416[/C][/ROW]
[ROW][C]17[/C][C]25[/C][C]23.7703627241227[/C][C]1.22963727587727[/C][/ROW]
[ROW][C]18[/C][C]22[/C][C]21.9316376495145[/C][C]0.0683623504855226[/C][/ROW]
[ROW][C]19[/C][C]26[/C][C]23.5846611998534[/C][C]2.41533880014656[/C][/ROW]
[ROW][C]20[/C][C]29[/C][C]23.6060930196684[/C][C]5.39390698033157[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]25.0407953432596[/C][C]6.95920465674042[/C][/ROW]
[ROW][C]22[/C][C]25[/C][C]22.2441924470041[/C][C]2.75580755299586[/C][/ROW]
[ROW][C]23[/C][C]28[/C][C]26.3173553384188[/C][C]1.68264466158119[/C][/ROW]
[ROW][C]24[/C][C]25[/C][C]23.6044936764052[/C][C]1.39550632359481[/C][/ROW]
[ROW][C]25[/C][C]25[/C][C]22.8312781012892[/C][C]2.16872189871082[/C][/ROW]
[ROW][C]26[/C][C]18[/C][C]21.7760006151216[/C][C]-3.77600061512158[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]24.8280182030008[/C][C]0.171981796999167[/C][/ROW]
[ROW][C]28[/C][C]25[/C][C]22.5256039357486[/C][C]2.47439606425144[/C][/ROW]
[ROW][C]29[/C][C]20[/C][C]24.0023423110419[/C][C]-4.00234231104185[/C][/ROW]
[ROW][C]30[/C][C]15[/C][C]16.5489407322832[/C][C]-1.54894073228325[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]25.2136967791899[/C][C]-1.21369677918993[/C][/ROW]
[ROW][C]32[/C][C]26[/C][C]24.2269251092524[/C][C]1.77307489074756[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]16.4988057281025[/C][C]-2.49880572810254[/C][/ROW]
[ROW][C]34[/C][C]24[/C][C]23.6882076241194[/C][C]0.31179237588064[/C][/ROW]
[ROW][C]35[/C][C]25[/C][C]22.2901142469709[/C][C]2.70988575302905[/C][/ROW]
[ROW][C]36[/C][C]20[/C][C]24.8228294406094[/C][C]-4.82282944060945[/C][/ROW]
[ROW][C]37[/C][C]21[/C][C]21.3379147072532[/C][C]-0.337914707253221[/C][/ROW]
[ROW][C]38[/C][C]27[/C][C]27.3806373577635[/C][C]-0.38063735776349[/C][/ROW]
[ROW][C]39[/C][C]23[/C][C]24.8875313004724[/C][C]-1.88753130047237[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]25.9236847186639[/C][C]-0.923684718663891[/C][/ROW]
[ROW][C]41[/C][C]20[/C][C]22.3048028084823[/C][C]-2.30480280848232[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]22.7351814508955[/C][C]-0.735181450895486[/C][/ROW]
[ROW][C]43[/C][C]25[/C][C]24.2033849886421[/C][C]0.796615011357932[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]23.4888364236610[/C][C]1.51116357633896[/C][/ROW]
[ROW][C]45[/C][C]17[/C][C]23.6354773260822[/C][C]-6.63547732608217[/C][/ROW]
[ROW][C]46[/C][C]25[/C][C]24.2370530452567[/C][C]0.762946954743253[/C][/ROW]
[ROW][C]47[/C][C]26[/C][C]22.6333412788056[/C][C]3.36665872119444[/C][/ROW]
[ROW][C]48[/C][C]27[/C][C]24.4833567931915[/C][C]2.51664320680848[/C][/ROW]
[ROW][C]49[/C][C]19[/C][C]20.034027614319[/C][C]-1.03402761431901[/C][/ROW]
[ROW][C]50[/C][C]22[/C][C]22.5712761469369[/C][C]-0.571276146936909[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]28.9933452561864[/C][C]3.00665474381355[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]24.7417584656952[/C][C]-3.74175846569519[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]21.5563365981081[/C][C]-3.55633659810815[/C][/ROW]
[ROW][C]54[/C][C]23[/C][C]23.1025501030034[/C][C]-0.10255010300339[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]21.8295927359971[/C][C]-1.82959273599709[/C][/ROW]
[ROW][C]56[/C][C]21[/C][C]22.6142313584396[/C][C]-1.61423135843962[/C][/ROW]
[ROW][C]57[/C][C]17[/C][C]19.2372963735279[/C][C]-2.23729637352785[/C][/ROW]
[ROW][C]58[/C][C]18[/C][C]20.3155783697159[/C][C]-2.31557836971593[/C][/ROW]
[ROW][C]59[/C][C]19[/C][C]20.9172996003486[/C][C]-1.91729960034864[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]22.4071052135827[/C][C]-0.407105213582672[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]19.2545777542679[/C][C]-5.25457775426786[/C][/ROW]
[ROW][C]62[/C][C]18[/C][C]26.5470290665061[/C][C]-8.54702906650608[/C][/ROW]
[ROW][C]63[/C][C]35[/C][C]23.9911319079473[/C][C]11.0088680920527[/C][/ROW]
[ROW][C]64[/C][C]29[/C][C]18.8830850110273[/C][C]10.1169149889727[/C][/ROW]
[ROW][C]65[/C][C]21[/C][C]22.3522465116134[/C][C]-1.35224651161343[/C][/ROW]
[ROW][C]66[/C][C]25[/C][C]21.2253467574949[/C][C]3.77465324250507[/C][/ROW]
[ROW][C]67[/C][C]26[/C][C]22.8371997271915[/C][C]3.16280027280852[/C][/ROW]
[ROW][C]68[/C][C]17[/C][C]17.5001681270367[/C][C]-0.500168127036699[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]20.4870454276716[/C][C]4.51295457232843[/C][/ROW]
[ROW][C]70[/C][C]20[/C][C]20.3710266060241[/C][C]-0.371026606024123[/C][/ROW]
[ROW][C]71[/C][C]22[/C][C]21.2232967322472[/C][C]0.776703267752821[/C][/ROW]
[ROW][C]72[/C][C]24[/C][C]22.6600076469483[/C][C]1.33999235305170[/C][/ROW]
[ROW][C]73[/C][C]21[/C][C]23.211232304497[/C][C]-2.211232304497[/C][/ROW]
[ROW][C]74[/C][C]26[/C][C]25.2122420087995[/C][C]0.787757991200536[/C][/ROW]
[ROW][C]75[/C][C]24[/C][C]20.7705608654128[/C][C]3.22943913458719[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]20.5381252902889[/C][C]-4.53812529028890[/C][/ROW]
[ROW][C]77[/C][C]18[/C][C]20.8620827426893[/C][C]-2.86208274268930[/C][/ROW]
[ROW][C]78[/C][C]19[/C][C]19.5746801581445[/C][C]-0.574680158144457[/C][/ROW]
[ROW][C]79[/C][C]21[/C][C]17.4860810651882[/C][C]3.51391893481175[/C][/ROW]
[ROW][C]80[/C][C]22[/C][C]18.6764148543751[/C][C]3.32358514562487[/C][/ROW]
[ROW][C]81[/C][C]23[/C][C]19.6406185486665[/C][C]3.35938145133348[/C][/ROW]
[ROW][C]82[/C][C]29[/C][C]24.9251318457098[/C][C]4.0748681542902[/C][/ROW]
[ROW][C]83[/C][C]21[/C][C]20.3323884410475[/C][C]0.667611558952548[/C][/ROW]
[ROW][C]84[/C][C]23[/C][C]22.4791527699975[/C][C]0.520847230002503[/C][/ROW]
[ROW][C]85[/C][C]27[/C][C]23.0933466330199[/C][C]3.90665336698007[/C][/ROW]
[ROW][C]86[/C][C]25[/C][C]25.665739034365[/C][C]-0.665739034365012[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]21.1025899837641[/C][C]-0.102589983764137[/C][/ROW]
[ROW][C]88[/C][C]10[/C][C]17.5450137047189[/C][C]-7.5450137047189[/C][/ROW]
[ROW][C]89[/C][C]20[/C][C]22.7748382661863[/C][C]-2.7748382661863[/C][/ROW]
[ROW][C]90[/C][C]26[/C][C]22.4024263631451[/C][C]3.59757363685486[/C][/ROW]
[ROW][C]91[/C][C]24[/C][C]24.1709732057960[/C][C]-0.170973205796026[/C][/ROW]
[ROW][C]92[/C][C]29[/C][C]32.039524903068[/C][C]-3.03952490306796[/C][/ROW]
[ROW][C]93[/C][C]19[/C][C]19.637559433908[/C][C]-0.637559433907986[/C][/ROW]
[ROW][C]94[/C][C]24[/C][C]22.8678535163986[/C][C]1.13214648360137[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]21.1918508495598[/C][C]-2.19185084955978[/C][/ROW]
[ROW][C]96[/C][C]22[/C][C]22.2332378774937[/C][C]-0.233237877493683[/C][/ROW]
[ROW][C]97[/C][C]17[/C][C]24.1770748831824[/C][C]-7.17707488318235[/C][/ROW]
[ROW][C]98[/C][C]24[/C][C]23.1891525756979[/C][C]0.810847424302144[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]20.1213719624976[/C][C]-1.12137196249761[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]22.2397545862169[/C][C]-3.2397545862169[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]19.0119528982715[/C][C]3.98804710172847[/C][/ROW]
[ROW][C]102[/C][C]27[/C][C]23.9996984663534[/C][C]3.00030153364658[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.0272740449086[/C][C]-1.02727404490862[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]23.0668347665730[/C][C]-1.06683476657305[/C][/ROW]
[ROW][C]105[/C][C]21[/C][C]22.480071673226[/C][C]-1.48007167322600[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]23.6785415443543[/C][C]-5.67854154435434[/C][/ROW]
[ROW][C]107[/C][C]20[/C][C]23.5712368913594[/C][C]-3.57123689135942[/C][/ROW]
[ROW][C]108[/C][C]19[/C][C]23.1012937277056[/C][C]-4.10129372770564[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]22.9231870073325[/C][C]1.07681299266752[/C][/ROW]
[ROW][C]110[/C][C]25[/C][C]26.0974028906659[/C][C]-1.09740289066594[/C][/ROW]
[ROW][C]111[/C][C]29[/C][C]24.5702919430365[/C][C]4.42970805696351[/C][/ROW]
[ROW][C]112[/C][C]28[/C][C]25.0080646376826[/C][C]2.99193536231745[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]15.8795947450843[/C][C]1.12040525491573[/C][/ROW]
[ROW][C]114[/C][C]29[/C][C]22.4470781605261[/C][C]6.55292183947392[/C][/ROW]
[ROW][C]115[/C][C]26[/C][C]26.9014120632511[/C][C]-0.901412063251095[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]17.8390211128728[/C][C]-3.83902111287284[/C][/ROW]
[ROW][C]117[/C][C]26[/C][C]21.6947080858076[/C][C]4.30529191419238[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]20.1628931194827[/C][C]-0.162893119482687[/C][/ROW]
[ROW][C]119[/C][C]32[/C][C]24.7378779732098[/C][C]7.2621220267902[/C][/ROW]
[ROW][C]120[/C][C]23[/C][C]21.2860663376028[/C][C]1.71393366239721[/C][/ROW]
[ROW][C]121[/C][C]21[/C][C]21.9949450332778[/C][C]-0.994945033277755[/C][/ROW]
[ROW][C]122[/C][C]30[/C][C]24.6954543998494[/C][C]5.3045456001506[/C][/ROW]
[ROW][C]123[/C][C]24[/C][C]20.8889578555672[/C][C]3.11104214443277[/C][/ROW]
[ROW][C]124[/C][C]22[/C][C]22.6345480739457[/C][C]-0.634548073945693[/C][/ROW]
[ROW][C]125[/C][C]24[/C][C]22.2611111441645[/C][C]1.73888885583554[/C][/ROW]
[ROW][C]126[/C][C]24[/C][C]22.4276344752233[/C][C]1.57236552477667[/C][/ROW]
[ROW][C]127[/C][C]24[/C][C]20.2771256019659[/C][C]3.72287439803414[/C][/ROW]
[ROW][C]128[/C][C]19[/C][C]18.0492519630842[/C][C]0.95074803691578[/C][/ROW]
[ROW][C]129[/C][C]31[/C][C]27.8184536659721[/C][C]3.18154633402794[/C][/ROW]
[ROW][C]130[/C][C]22[/C][C]26.8821117405066[/C][C]-4.88211174050664[/C][/ROW]
[ROW][C]131[/C][C]27[/C][C]20.7453100963267[/C][C]6.25468990367333[/C][/ROW]
[ROW][C]132[/C][C]19[/C][C]17.5094673012092[/C][C]1.49053269879080[/C][/ROW]
[ROW][C]133[/C][C]21[/C][C]18.4446855636216[/C][C]2.55531443637837[/C][/ROW]
[ROW][C]134[/C][C]23[/C][C]23.6545548145521[/C][C]-0.654554814552146[/C][/ROW]
[ROW][C]135[/C][C]19[/C][C]20.62733266948[/C][C]-1.62733266948002[/C][/ROW]
[ROW][C]136[/C][C]19[/C][C]22.8328678522258[/C][C]-3.83286785222581[/C][/ROW]
[ROW][C]137[/C][C]20[/C][C]22.0710595177597[/C][C]-2.07105951775966[/C][/ROW]
[ROW][C]138[/C][C]23[/C][C]21.3932453453271[/C][C]1.60675465467294[/C][/ROW]
[ROW][C]139[/C][C]17[/C][C]20.2204281949549[/C][C]-3.22042819495491[/C][/ROW]
[ROW][C]140[/C][C]17[/C][C]22.5158548399329[/C][C]-5.51585483993291[/C][/ROW]
[ROW][C]141[/C][C]17[/C][C]19.6490080600547[/C][C]-2.64900806005468[/C][/ROW]
[ROW][C]142[/C][C]21[/C][C]24.0813306672545[/C][C]-3.08133066725446[/C][/ROW]
[ROW][C]143[/C][C]21[/C][C]23.9449545369319[/C][C]-2.94495453693186[/C][/ROW]
[ROW][C]144[/C][C]18[/C][C]20.6650625429321[/C][C]-2.66506254293213[/C][/ROW]
[ROW][C]145[/C][C]19[/C][C]20.1098547493404[/C][C]-1.10985474934042[/C][/ROW]
[ROW][C]146[/C][C]20[/C][C]24.2900101615384[/C][C]-4.29001016153839[/C][/ROW]
[ROW][C]147[/C][C]15[/C][C]17.6814817408554[/C][C]-2.68148174085542[/C][/ROW]
[ROW][C]148[/C][C]24[/C][C]21.5555549121971[/C][C]2.44444508780294[/C][/ROW]
[ROW][C]149[/C][C]20[/C][C]17.9399506496497[/C][C]2.06004935035035[/C][/ROW]
[ROW][C]150[/C][C]22[/C][C]22.2540686796497[/C][C]-0.254068679649664[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]15.6183134640767[/C][C]-2.61831346407669[/C][/ROW]
[ROW][C]152[/C][C]19[/C][C]17.8858937998625[/C][C]1.11410620013752[/C][/ROW]
[ROW][C]153[/C][C]21[/C][C]21.2774151677469[/C][C]-0.277415167746949[/C][/ROW]
[ROW][C]154[/C][C]23[/C][C]22.4836968759943[/C][C]0.516303124005703[/C][/ROW]
[ROW][C]155[/C][C]16[/C][C]22.0445536366044[/C][C]-6.04455363660442[/C][/ROW]
[ROW][C]156[/C][C]26[/C][C]22.0728915894783[/C][C]3.92710841052168[/C][/ROW]
[ROW][C]157[/C][C]21[/C][C]22.1738873024122[/C][C]-1.17388730241222[/C][/ROW]
[ROW][C]158[/C][C]21[/C][C]20.1046855891451[/C][C]0.89531441085486[/C][/ROW]
[ROW][C]159[/C][C]24[/C][C]23.1882045116100[/C][C]0.811795488390044[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98824&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98824&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12521.88228318778823.11771681221184
23024.69969795905795.30030204094214
32220.67283947823531.32716052176470
42223.2172477003945-1.21724770039453
52522.73996096544022.26003903455985
62319.51969509343213.48030490656795
71719.3385350459548-2.33853504595485
82122.0094817127892-1.00948171278924
91923.7341169344749-4.7341169344749
101522.9461870992115-7.94618709921148
111617.3941561848707-1.39415618487066
122220.75478193400671.24521806599330
132324.3977577855181-1.39775778551813
142321.60128625311071.39871374688932
151922.4316112343517-3.43161123435169
162322.79350634595760.206493654042416
172523.77036272412271.22963727587727
182221.93163764951450.0683623504855226
192623.58466119985342.41533880014656
202923.60609301966845.39390698033157
213225.04079534325966.95920465674042
222522.24419244700412.75580755299586
232826.31735533841881.68264466158119
242523.60449367640521.39550632359481
252522.83127810128922.16872189871082
261821.7760006151216-3.77600061512158
272524.82801820300080.171981796999167
282522.52560393574862.47439606425144
292024.0023423110419-4.00234231104185
301516.5489407322832-1.54894073228325
312425.2136967791899-1.21369677918993
322624.22692510925241.77307489074756
331416.4988057281025-2.49880572810254
342423.68820762411940.31179237588064
352522.29011424697092.70988575302905
362024.8228294406094-4.82282944060945
372121.3379147072532-0.337914707253221
382727.3806373577635-0.38063735776349
392324.8875313004724-1.88753130047237
402525.9236847186639-0.923684718663891
412022.3048028084823-2.30480280848232
422222.7351814508955-0.735181450895486
432524.20338498864210.796615011357932
442523.48883642366101.51116357633896
451723.6354773260822-6.63547732608217
462524.23705304525670.762946954743253
472622.63334127880563.36665872119444
482724.48335679319152.51664320680848
491920.034027614319-1.03402761431901
502222.5712761469369-0.571276146936909
513228.99334525618643.00665474381355
522124.7417584656952-3.74175846569519
531821.5563365981081-3.55633659810815
542323.1025501030034-0.10255010300339
552021.8295927359971-1.82959273599709
562122.6142313584396-1.61423135843962
571719.2372963735279-2.23729637352785
581820.3155783697159-2.31557836971593
591920.9172996003486-1.91729960034864
602222.4071052135827-0.407105213582672
611419.2545777542679-5.25457775426786
621826.5470290665061-8.54702906650608
633523.991131907947311.0088680920527
642918.883085011027310.1169149889727
652122.3522465116134-1.35224651161343
662521.22534675749493.77465324250507
672622.83719972719153.16280027280852
681717.5001681270367-0.500168127036699
692520.48704542767164.51295457232843
702020.3710266060241-0.371026606024123
712221.22329673224720.776703267752821
722422.66000764694831.33999235305170
732123.211232304497-2.211232304497
742625.21224200879950.787757991200536
752420.77056086541283.22943913458719
761620.5381252902889-4.53812529028890
771820.8620827426893-2.86208274268930
781919.5746801581445-0.574680158144457
792117.48608106518823.51391893481175
802218.67641485437513.32358514562487
812319.64061854866653.35938145133348
822924.92513184570984.0748681542902
832120.33238844104750.667611558952548
842322.47915276999750.520847230002503
852723.09334663301993.90665336698007
862525.665739034365-0.665739034365012
872121.1025899837641-0.102589983764137
881017.5450137047189-7.5450137047189
892022.7748382661863-2.7748382661863
902622.40242636314513.59757363685486
912424.1709732057960-0.170973205796026
922932.039524903068-3.03952490306796
931919.637559433908-0.637559433907986
942422.86785351639861.13214648360137
951921.1918508495598-2.19185084955978
962222.2332378774937-0.233237877493683
971724.1770748831824-7.17707488318235
982423.18915257569790.810847424302144
991920.1213719624976-1.12137196249761
1001922.2397545862169-3.2397545862169
1012319.01195289827153.98804710172847
1022723.99969846635343.00030153364658
1031415.0272740449086-1.02727404490862
1042223.0668347665730-1.06683476657305
1052122.480071673226-1.48007167322600
1061823.6785415443543-5.67854154435434
1072023.5712368913594-3.57123689135942
1081923.1012937277056-4.10129372770564
1092422.92318700733251.07681299266752
1102526.0974028906659-1.09740289066594
1112924.57029194303654.42970805696351
1122825.00806463768262.99193536231745
1131715.87959474508431.12040525491573
1142922.44707816052616.55292183947392
1152626.9014120632511-0.901412063251095
1161417.8390211128728-3.83902111287284
1172621.69470808580764.30529191419238
1182020.1628931194827-0.162893119482687
1193224.73787797320987.2621220267902
1202321.28606633760281.71393366239721
1212121.9949450332778-0.994945033277755
1223024.69545439984945.3045456001506
1232420.88895785556723.11104214443277
1242222.6345480739457-0.634548073945693
1252422.26111114416451.73888885583554
1262422.42763447522331.57236552477667
1272420.27712560196593.72287439803414
1281918.04925196308420.95074803691578
1293127.81845366597213.18154633402794
1302226.8821117405066-4.88211174050664
1312720.74531009632676.25468990367333
1321917.50946730120921.49053269879080
1332118.44468556362162.55531443637837
1342323.6545548145521-0.654554814552146
1351920.62733266948-1.62733266948002
1361922.8328678522258-3.83286785222581
1372022.0710595177597-2.07105951775966
1382321.39324534532711.60675465467294
1391720.2204281949549-3.22042819495491
1401722.5158548399329-5.51585483993291
1411719.6490080600547-2.64900806005468
1422124.0813306672545-3.08133066725446
1432123.9449545369319-2.94495453693186
1441820.6650625429321-2.66506254293213
1451920.1098547493404-1.10985474934042
1462024.2900101615384-4.29001016153839
1471517.6814817408554-2.68148174085542
1482421.55555491219712.44444508780294
1492017.93995064964972.06004935035035
1502222.2540686796497-0.254068679649664
1511315.6183134640767-2.61831346407669
1521917.88589379986251.11410620013752
1532121.2774151677469-0.277415167746949
1542322.48369687599430.516303124005703
1551622.0445536366044-6.04455363660442
1562622.07289158947833.92710841052168
1572122.1738873024122-1.17388730241222
1582120.10468558914510.89531441085486
1592423.18820451161000.811795488390044







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9910899284354470.01782014312910540.00891007156455268
160.9789930761537670.04201384769246570.0210069238462329
170.9582323405019660.08353531899606850.0417676594980342
180.9284799737602770.1430400524794460.071520026239723
190.9219148945979650.1561702108040700.0780851054020348
200.9408727456067960.1182545087864080.0591272543932041
210.9676516166485740.06469676670285130.0323483833514256
220.9541071228191720.0917857543616570.0458928771808285
230.9315329853877780.1369340292244440.0684670146122221
240.9008286886933320.1983426226133360.0991713113066681
250.8666276132730070.2667447734539850.133372386726993
260.8645066872324540.2709866255350920.135493312767546
270.8216655475927470.3566689048145070.178334452407253
280.7817841672645940.4364316654708120.218215832735406
290.8069567644268970.3860864711462070.193043235573103
300.7645940335754440.4708119328491110.235405966424556
310.7150036960600880.5699926078798250.284996303939912
320.665223359444430.6695532811111390.334776640555569
330.6142927271582520.7714145456834960.385707272841748
340.5506093870421390.8987812259157230.449390612957861
350.52831696894550.9433660621090.4716830310545
360.6254470780098280.7491058439803430.374552921990172
370.5643906437476070.8712187125047860.435609356252393
380.5088395570037480.9823208859925050.491160442996252
390.4669482941092810.9338965882185610.53305170589072
400.4274238804641960.8548477609283920.572576119535804
410.3908752351400140.781750470280030.609124764859986
420.3369580030707020.6739160061414040.663041996929298
430.2865077861800040.5730155723600080.713492213819996
440.2519517143830370.5039034287660730.748048285616963
450.3904741840758620.7809483681517240.609525815924138
460.3386030675866090.6772061351732190.66139693241339
470.3304018060441870.6608036120883740.669598193955813
480.3202807385788840.6405614771577680.679719261421116
490.2742916240703540.5485832481407070.725708375929646
500.2369299778135010.4738599556270030.763070022186499
510.2178286772160260.4356573544320510.782171322783974
520.2285800481113170.4571600962226340.771419951888683
530.2257612863858170.4515225727716330.774238713614184
540.1873304454767870.3746608909535730.812669554523213
550.1618906902962480.3237813805924950.838109309703752
560.1358184491857770.2716368983715550.864181550814223
570.1156895631441210.2313791262882420.884310436855879
580.09954429521469080.1990885904293820.90045570478531
590.08452767930802990.1690553586160600.91547232069197
600.06613968610465870.1322793722093170.933860313895341
610.08447735044225120.1689547008845020.915522649557749
620.2537801870533520.5075603741067030.746219812946648
630.702129226138420.5957415477231590.297870773861580
640.9239344970264530.1521310059470930.0760655029735466
650.908202957896550.1835940842069000.0917970421034502
660.9192124399007950.1615751201984110.0807875600992054
670.9184233746268010.1631532507463970.0815766253731985
680.8982855573165610.2034288853668780.101714442683439
690.910006700788320.1799865984233590.0899932992116797
700.8890990090561770.2218019818876470.110900990943823
710.8686567982317460.2626864035365070.131343201768254
720.8458434157318280.3083131685363450.154156584268172
730.824269337496930.3514613250061390.175730662503070
740.7967575977333030.4064848045333930.203242402266697
750.7898334112383260.4203331775233470.210166588761674
760.8024100468248460.3951799063503080.197589953175154
770.7883846211675220.4232307576649550.211615378832478
780.7533839317895210.4932321364209580.246616068210479
790.7539014819782030.4921970360435950.246098518021797
800.7380617471950060.5238765056099890.261938252804994
810.7244221802043560.5511556395912870.275577819795644
820.744869339141940.510261321716120.25513066085806
830.7724884214963880.4550231570072250.227511578503612
840.738852375180550.5222952496388990.261147624819450
850.7477066064162830.5045867871674330.252293393583717
860.7236713366336890.5526573267326220.276328663366311
870.6817299099743560.6365401800512870.318270090025644
880.784955498359490.4300890032810210.215044501640510
890.7694338389856870.4611323220286270.230566161014313
900.7990465460492160.4019069079015680.200953453950784
910.7772335857884980.4455328284230040.222766414211502
920.7531964358292880.4936071283414240.246803564170712
930.712325856919010.575348286161980.28767414308099
940.6687766693251210.6624466613497590.331223330674879
950.627791860221220.744416279557560.37220813977878
960.5794361222898430.8411277554203130.420563877710157
970.6108031917211430.7783936165577140.389196808278857
980.563059882521690.8738802349566210.436940117478311
990.5154197262827140.9691605474345730.484580273717286
1000.4933080760481080.9866161520962150.506691923951892
1010.4831562049535390.9663124099070780.516843795046461
1020.4679533483025020.9359066966050030.532046651697498
1030.4209042247794340.8418084495588690.579095775220566
1040.3894791192913450.778958238582690.610520880708655
1050.3625253520151460.7250507040302930.637474647984854
1060.4333242501413090.8666485002826180.566675749858691
1070.4288956741352220.8577913482704440.571104325864778
1080.4314114293317380.8628228586634760.568588570668262
1090.3903700711076530.7807401422153060.609629928892347
1100.3527407320545520.7054814641091050.647259267945448
1110.3681909266559840.7363818533119670.631809073344016
1120.3663249384925120.7326498769850240.633675061507488
1130.3257038791256830.6514077582513660.674296120874317
1140.4485440383779660.8970880767559320.551455961622034
1150.3947966711196650.7895933422393310.605203328880335
1160.4061951530916270.8123903061832550.593804846908373
1170.4285637707958350.857127541591670.571436229204165
1180.3721167197848500.7442334395696990.62788328021515
1190.6212112166464730.7575775667070550.378788783353527
1200.6405588630339360.7188822739321270.359441136966064
1210.5871040629162220.8257918741675550.412895937083778
1220.6251051064369650.7497897871260690.374894893563034
1230.617444449578850.76511110084230.38255555042115
1240.5554869265145070.8890261469709850.444513073485493
1250.504277768860230.991444462279540.49572223113977
1260.4705499176530220.9410998353060440.529450082346978
1270.4800325126798730.9600650253597460.519967487320127
1280.4126649224145940.8253298448291870.587335077585406
1290.635141235341140.729717529317720.36485876465886
1300.5943010029171090.8113979941657820.405698997082891
1310.7994014648230380.4011970703539240.200598535176962
1320.7382036274549240.5235927450901530.261796372545076
1330.6886521006771160.6226957986457670.311347899322883
1340.619324781585880.761350436828240.38067521841412
1350.5429985982799520.9140028034400970.457001401720048
1360.4680367935845620.9360735871691250.531963206415438
1370.3822355398996550.764471079799310.617764460100345
1380.3261071844389640.6522143688779270.673892815561036
1390.2722577657705690.5445155315411390.72774223422943
1400.3083379836165090.6166759672330190.69166201638349
1410.2251189626066460.4502379252132910.774881037393354
1420.1589199816885000.3178399633770010.8410800183115
1430.1161922857274140.2323845714548280.883807714272586
1440.06795274302209460.1359054860441890.932047256977905

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.991089928435447 & 0.0178201431291054 & 0.00891007156455268 \tabularnewline
16 & 0.978993076153767 & 0.0420138476924657 & 0.0210069238462329 \tabularnewline
17 & 0.958232340501966 & 0.0835353189960685 & 0.0417676594980342 \tabularnewline
18 & 0.928479973760277 & 0.143040052479446 & 0.071520026239723 \tabularnewline
19 & 0.921914894597965 & 0.156170210804070 & 0.0780851054020348 \tabularnewline
20 & 0.940872745606796 & 0.118254508786408 & 0.0591272543932041 \tabularnewline
21 & 0.967651616648574 & 0.0646967667028513 & 0.0323483833514256 \tabularnewline
22 & 0.954107122819172 & 0.091785754361657 & 0.0458928771808285 \tabularnewline
23 & 0.931532985387778 & 0.136934029224444 & 0.0684670146122221 \tabularnewline
24 & 0.900828688693332 & 0.198342622613336 & 0.0991713113066681 \tabularnewline
25 & 0.866627613273007 & 0.266744773453985 & 0.133372386726993 \tabularnewline
26 & 0.864506687232454 & 0.270986625535092 & 0.135493312767546 \tabularnewline
27 & 0.821665547592747 & 0.356668904814507 & 0.178334452407253 \tabularnewline
28 & 0.781784167264594 & 0.436431665470812 & 0.218215832735406 \tabularnewline
29 & 0.806956764426897 & 0.386086471146207 & 0.193043235573103 \tabularnewline
30 & 0.764594033575444 & 0.470811932849111 & 0.235405966424556 \tabularnewline
31 & 0.715003696060088 & 0.569992607879825 & 0.284996303939912 \tabularnewline
32 & 0.66522335944443 & 0.669553281111139 & 0.334776640555569 \tabularnewline
33 & 0.614292727158252 & 0.771414545683496 & 0.385707272841748 \tabularnewline
34 & 0.550609387042139 & 0.898781225915723 & 0.449390612957861 \tabularnewline
35 & 0.5283169689455 & 0.943366062109 & 0.4716830310545 \tabularnewline
36 & 0.625447078009828 & 0.749105843980343 & 0.374552921990172 \tabularnewline
37 & 0.564390643747607 & 0.871218712504786 & 0.435609356252393 \tabularnewline
38 & 0.508839557003748 & 0.982320885992505 & 0.491160442996252 \tabularnewline
39 & 0.466948294109281 & 0.933896588218561 & 0.53305170589072 \tabularnewline
40 & 0.427423880464196 & 0.854847760928392 & 0.572576119535804 \tabularnewline
41 & 0.390875235140014 & 0.78175047028003 & 0.609124764859986 \tabularnewline
42 & 0.336958003070702 & 0.673916006141404 & 0.663041996929298 \tabularnewline
43 & 0.286507786180004 & 0.573015572360008 & 0.713492213819996 \tabularnewline
44 & 0.251951714383037 & 0.503903428766073 & 0.748048285616963 \tabularnewline
45 & 0.390474184075862 & 0.780948368151724 & 0.609525815924138 \tabularnewline
46 & 0.338603067586609 & 0.677206135173219 & 0.66139693241339 \tabularnewline
47 & 0.330401806044187 & 0.660803612088374 & 0.669598193955813 \tabularnewline
48 & 0.320280738578884 & 0.640561477157768 & 0.679719261421116 \tabularnewline
49 & 0.274291624070354 & 0.548583248140707 & 0.725708375929646 \tabularnewline
50 & 0.236929977813501 & 0.473859955627003 & 0.763070022186499 \tabularnewline
51 & 0.217828677216026 & 0.435657354432051 & 0.782171322783974 \tabularnewline
52 & 0.228580048111317 & 0.457160096222634 & 0.771419951888683 \tabularnewline
53 & 0.225761286385817 & 0.451522572771633 & 0.774238713614184 \tabularnewline
54 & 0.187330445476787 & 0.374660890953573 & 0.812669554523213 \tabularnewline
55 & 0.161890690296248 & 0.323781380592495 & 0.838109309703752 \tabularnewline
56 & 0.135818449185777 & 0.271636898371555 & 0.864181550814223 \tabularnewline
57 & 0.115689563144121 & 0.231379126288242 & 0.884310436855879 \tabularnewline
58 & 0.0995442952146908 & 0.199088590429382 & 0.90045570478531 \tabularnewline
59 & 0.0845276793080299 & 0.169055358616060 & 0.91547232069197 \tabularnewline
60 & 0.0661396861046587 & 0.132279372209317 & 0.933860313895341 \tabularnewline
61 & 0.0844773504422512 & 0.168954700884502 & 0.915522649557749 \tabularnewline
62 & 0.253780187053352 & 0.507560374106703 & 0.746219812946648 \tabularnewline
63 & 0.70212922613842 & 0.595741547723159 & 0.297870773861580 \tabularnewline
64 & 0.923934497026453 & 0.152131005947093 & 0.0760655029735466 \tabularnewline
65 & 0.90820295789655 & 0.183594084206900 & 0.0917970421034502 \tabularnewline
66 & 0.919212439900795 & 0.161575120198411 & 0.0807875600992054 \tabularnewline
67 & 0.918423374626801 & 0.163153250746397 & 0.0815766253731985 \tabularnewline
68 & 0.898285557316561 & 0.203428885366878 & 0.101714442683439 \tabularnewline
69 & 0.91000670078832 & 0.179986598423359 & 0.0899932992116797 \tabularnewline
70 & 0.889099009056177 & 0.221801981887647 & 0.110900990943823 \tabularnewline
71 & 0.868656798231746 & 0.262686403536507 & 0.131343201768254 \tabularnewline
72 & 0.845843415731828 & 0.308313168536345 & 0.154156584268172 \tabularnewline
73 & 0.82426933749693 & 0.351461325006139 & 0.175730662503070 \tabularnewline
74 & 0.796757597733303 & 0.406484804533393 & 0.203242402266697 \tabularnewline
75 & 0.789833411238326 & 0.420333177523347 & 0.210166588761674 \tabularnewline
76 & 0.802410046824846 & 0.395179906350308 & 0.197589953175154 \tabularnewline
77 & 0.788384621167522 & 0.423230757664955 & 0.211615378832478 \tabularnewline
78 & 0.753383931789521 & 0.493232136420958 & 0.246616068210479 \tabularnewline
79 & 0.753901481978203 & 0.492197036043595 & 0.246098518021797 \tabularnewline
80 & 0.738061747195006 & 0.523876505609989 & 0.261938252804994 \tabularnewline
81 & 0.724422180204356 & 0.551155639591287 & 0.275577819795644 \tabularnewline
82 & 0.74486933914194 & 0.51026132171612 & 0.25513066085806 \tabularnewline
83 & 0.772488421496388 & 0.455023157007225 & 0.227511578503612 \tabularnewline
84 & 0.73885237518055 & 0.522295249638899 & 0.261147624819450 \tabularnewline
85 & 0.747706606416283 & 0.504586787167433 & 0.252293393583717 \tabularnewline
86 & 0.723671336633689 & 0.552657326732622 & 0.276328663366311 \tabularnewline
87 & 0.681729909974356 & 0.636540180051287 & 0.318270090025644 \tabularnewline
88 & 0.78495549835949 & 0.430089003281021 & 0.215044501640510 \tabularnewline
89 & 0.769433838985687 & 0.461132322028627 & 0.230566161014313 \tabularnewline
90 & 0.799046546049216 & 0.401906907901568 & 0.200953453950784 \tabularnewline
91 & 0.777233585788498 & 0.445532828423004 & 0.222766414211502 \tabularnewline
92 & 0.753196435829288 & 0.493607128341424 & 0.246803564170712 \tabularnewline
93 & 0.71232585691901 & 0.57534828616198 & 0.28767414308099 \tabularnewline
94 & 0.668776669325121 & 0.662446661349759 & 0.331223330674879 \tabularnewline
95 & 0.62779186022122 & 0.74441627955756 & 0.37220813977878 \tabularnewline
96 & 0.579436122289843 & 0.841127755420313 & 0.420563877710157 \tabularnewline
97 & 0.610803191721143 & 0.778393616557714 & 0.389196808278857 \tabularnewline
98 & 0.56305988252169 & 0.873880234956621 & 0.436940117478311 \tabularnewline
99 & 0.515419726282714 & 0.969160547434573 & 0.484580273717286 \tabularnewline
100 & 0.493308076048108 & 0.986616152096215 & 0.506691923951892 \tabularnewline
101 & 0.483156204953539 & 0.966312409907078 & 0.516843795046461 \tabularnewline
102 & 0.467953348302502 & 0.935906696605003 & 0.532046651697498 \tabularnewline
103 & 0.420904224779434 & 0.841808449558869 & 0.579095775220566 \tabularnewline
104 & 0.389479119291345 & 0.77895823858269 & 0.610520880708655 \tabularnewline
105 & 0.362525352015146 & 0.725050704030293 & 0.637474647984854 \tabularnewline
106 & 0.433324250141309 & 0.866648500282618 & 0.566675749858691 \tabularnewline
107 & 0.428895674135222 & 0.857791348270444 & 0.571104325864778 \tabularnewline
108 & 0.431411429331738 & 0.862822858663476 & 0.568588570668262 \tabularnewline
109 & 0.390370071107653 & 0.780740142215306 & 0.609629928892347 \tabularnewline
110 & 0.352740732054552 & 0.705481464109105 & 0.647259267945448 \tabularnewline
111 & 0.368190926655984 & 0.736381853311967 & 0.631809073344016 \tabularnewline
112 & 0.366324938492512 & 0.732649876985024 & 0.633675061507488 \tabularnewline
113 & 0.325703879125683 & 0.651407758251366 & 0.674296120874317 \tabularnewline
114 & 0.448544038377966 & 0.897088076755932 & 0.551455961622034 \tabularnewline
115 & 0.394796671119665 & 0.789593342239331 & 0.605203328880335 \tabularnewline
116 & 0.406195153091627 & 0.812390306183255 & 0.593804846908373 \tabularnewline
117 & 0.428563770795835 & 0.85712754159167 & 0.571436229204165 \tabularnewline
118 & 0.372116719784850 & 0.744233439569699 & 0.62788328021515 \tabularnewline
119 & 0.621211216646473 & 0.757577566707055 & 0.378788783353527 \tabularnewline
120 & 0.640558863033936 & 0.718882273932127 & 0.359441136966064 \tabularnewline
121 & 0.587104062916222 & 0.825791874167555 & 0.412895937083778 \tabularnewline
122 & 0.625105106436965 & 0.749789787126069 & 0.374894893563034 \tabularnewline
123 & 0.61744444957885 & 0.7651111008423 & 0.38255555042115 \tabularnewline
124 & 0.555486926514507 & 0.889026146970985 & 0.444513073485493 \tabularnewline
125 & 0.50427776886023 & 0.99144446227954 & 0.49572223113977 \tabularnewline
126 & 0.470549917653022 & 0.941099835306044 & 0.529450082346978 \tabularnewline
127 & 0.480032512679873 & 0.960065025359746 & 0.519967487320127 \tabularnewline
128 & 0.412664922414594 & 0.825329844829187 & 0.587335077585406 \tabularnewline
129 & 0.63514123534114 & 0.72971752931772 & 0.36485876465886 \tabularnewline
130 & 0.594301002917109 & 0.811397994165782 & 0.405698997082891 \tabularnewline
131 & 0.799401464823038 & 0.401197070353924 & 0.200598535176962 \tabularnewline
132 & 0.738203627454924 & 0.523592745090153 & 0.261796372545076 \tabularnewline
133 & 0.688652100677116 & 0.622695798645767 & 0.311347899322883 \tabularnewline
134 & 0.61932478158588 & 0.76135043682824 & 0.38067521841412 \tabularnewline
135 & 0.542998598279952 & 0.914002803440097 & 0.457001401720048 \tabularnewline
136 & 0.468036793584562 & 0.936073587169125 & 0.531963206415438 \tabularnewline
137 & 0.382235539899655 & 0.76447107979931 & 0.617764460100345 \tabularnewline
138 & 0.326107184438964 & 0.652214368877927 & 0.673892815561036 \tabularnewline
139 & 0.272257765770569 & 0.544515531541139 & 0.72774223422943 \tabularnewline
140 & 0.308337983616509 & 0.616675967233019 & 0.69166201638349 \tabularnewline
141 & 0.225118962606646 & 0.450237925213291 & 0.774881037393354 \tabularnewline
142 & 0.158919981688500 & 0.317839963377001 & 0.8410800183115 \tabularnewline
143 & 0.116192285727414 & 0.232384571454828 & 0.883807714272586 \tabularnewline
144 & 0.0679527430220946 & 0.135905486044189 & 0.932047256977905 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98824&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]15[/C][C]0.991089928435447[/C][C]0.0178201431291054[/C][C]0.00891007156455268[/C][/ROW]
[ROW][C]16[/C][C]0.978993076153767[/C][C]0.0420138476924657[/C][C]0.0210069238462329[/C][/ROW]
[ROW][C]17[/C][C]0.958232340501966[/C][C]0.0835353189960685[/C][C]0.0417676594980342[/C][/ROW]
[ROW][C]18[/C][C]0.928479973760277[/C][C]0.143040052479446[/C][C]0.071520026239723[/C][/ROW]
[ROW][C]19[/C][C]0.921914894597965[/C][C]0.156170210804070[/C][C]0.0780851054020348[/C][/ROW]
[ROW][C]20[/C][C]0.940872745606796[/C][C]0.118254508786408[/C][C]0.0591272543932041[/C][/ROW]
[ROW][C]21[/C][C]0.967651616648574[/C][C]0.0646967667028513[/C][C]0.0323483833514256[/C][/ROW]
[ROW][C]22[/C][C]0.954107122819172[/C][C]0.091785754361657[/C][C]0.0458928771808285[/C][/ROW]
[ROW][C]23[/C][C]0.931532985387778[/C][C]0.136934029224444[/C][C]0.0684670146122221[/C][/ROW]
[ROW][C]24[/C][C]0.900828688693332[/C][C]0.198342622613336[/C][C]0.0991713113066681[/C][/ROW]
[ROW][C]25[/C][C]0.866627613273007[/C][C]0.266744773453985[/C][C]0.133372386726993[/C][/ROW]
[ROW][C]26[/C][C]0.864506687232454[/C][C]0.270986625535092[/C][C]0.135493312767546[/C][/ROW]
[ROW][C]27[/C][C]0.821665547592747[/C][C]0.356668904814507[/C][C]0.178334452407253[/C][/ROW]
[ROW][C]28[/C][C]0.781784167264594[/C][C]0.436431665470812[/C][C]0.218215832735406[/C][/ROW]
[ROW][C]29[/C][C]0.806956764426897[/C][C]0.386086471146207[/C][C]0.193043235573103[/C][/ROW]
[ROW][C]30[/C][C]0.764594033575444[/C][C]0.470811932849111[/C][C]0.235405966424556[/C][/ROW]
[ROW][C]31[/C][C]0.715003696060088[/C][C]0.569992607879825[/C][C]0.284996303939912[/C][/ROW]
[ROW][C]32[/C][C]0.66522335944443[/C][C]0.669553281111139[/C][C]0.334776640555569[/C][/ROW]
[ROW][C]33[/C][C]0.614292727158252[/C][C]0.771414545683496[/C][C]0.385707272841748[/C][/ROW]
[ROW][C]34[/C][C]0.550609387042139[/C][C]0.898781225915723[/C][C]0.449390612957861[/C][/ROW]
[ROW][C]35[/C][C]0.5283169689455[/C][C]0.943366062109[/C][C]0.4716830310545[/C][/ROW]
[ROW][C]36[/C][C]0.625447078009828[/C][C]0.749105843980343[/C][C]0.374552921990172[/C][/ROW]
[ROW][C]37[/C][C]0.564390643747607[/C][C]0.871218712504786[/C][C]0.435609356252393[/C][/ROW]
[ROW][C]38[/C][C]0.508839557003748[/C][C]0.982320885992505[/C][C]0.491160442996252[/C][/ROW]
[ROW][C]39[/C][C]0.466948294109281[/C][C]0.933896588218561[/C][C]0.53305170589072[/C][/ROW]
[ROW][C]40[/C][C]0.427423880464196[/C][C]0.854847760928392[/C][C]0.572576119535804[/C][/ROW]
[ROW][C]41[/C][C]0.390875235140014[/C][C]0.78175047028003[/C][C]0.609124764859986[/C][/ROW]
[ROW][C]42[/C][C]0.336958003070702[/C][C]0.673916006141404[/C][C]0.663041996929298[/C][/ROW]
[ROW][C]43[/C][C]0.286507786180004[/C][C]0.573015572360008[/C][C]0.713492213819996[/C][/ROW]
[ROW][C]44[/C][C]0.251951714383037[/C][C]0.503903428766073[/C][C]0.748048285616963[/C][/ROW]
[ROW][C]45[/C][C]0.390474184075862[/C][C]0.780948368151724[/C][C]0.609525815924138[/C][/ROW]
[ROW][C]46[/C][C]0.338603067586609[/C][C]0.677206135173219[/C][C]0.66139693241339[/C][/ROW]
[ROW][C]47[/C][C]0.330401806044187[/C][C]0.660803612088374[/C][C]0.669598193955813[/C][/ROW]
[ROW][C]48[/C][C]0.320280738578884[/C][C]0.640561477157768[/C][C]0.679719261421116[/C][/ROW]
[ROW][C]49[/C][C]0.274291624070354[/C][C]0.548583248140707[/C][C]0.725708375929646[/C][/ROW]
[ROW][C]50[/C][C]0.236929977813501[/C][C]0.473859955627003[/C][C]0.763070022186499[/C][/ROW]
[ROW][C]51[/C][C]0.217828677216026[/C][C]0.435657354432051[/C][C]0.782171322783974[/C][/ROW]
[ROW][C]52[/C][C]0.228580048111317[/C][C]0.457160096222634[/C][C]0.771419951888683[/C][/ROW]
[ROW][C]53[/C][C]0.225761286385817[/C][C]0.451522572771633[/C][C]0.774238713614184[/C][/ROW]
[ROW][C]54[/C][C]0.187330445476787[/C][C]0.374660890953573[/C][C]0.812669554523213[/C][/ROW]
[ROW][C]55[/C][C]0.161890690296248[/C][C]0.323781380592495[/C][C]0.838109309703752[/C][/ROW]
[ROW][C]56[/C][C]0.135818449185777[/C][C]0.271636898371555[/C][C]0.864181550814223[/C][/ROW]
[ROW][C]57[/C][C]0.115689563144121[/C][C]0.231379126288242[/C][C]0.884310436855879[/C][/ROW]
[ROW][C]58[/C][C]0.0995442952146908[/C][C]0.199088590429382[/C][C]0.90045570478531[/C][/ROW]
[ROW][C]59[/C][C]0.0845276793080299[/C][C]0.169055358616060[/C][C]0.91547232069197[/C][/ROW]
[ROW][C]60[/C][C]0.0661396861046587[/C][C]0.132279372209317[/C][C]0.933860313895341[/C][/ROW]
[ROW][C]61[/C][C]0.0844773504422512[/C][C]0.168954700884502[/C][C]0.915522649557749[/C][/ROW]
[ROW][C]62[/C][C]0.253780187053352[/C][C]0.507560374106703[/C][C]0.746219812946648[/C][/ROW]
[ROW][C]63[/C][C]0.70212922613842[/C][C]0.595741547723159[/C][C]0.297870773861580[/C][/ROW]
[ROW][C]64[/C][C]0.923934497026453[/C][C]0.152131005947093[/C][C]0.0760655029735466[/C][/ROW]
[ROW][C]65[/C][C]0.90820295789655[/C][C]0.183594084206900[/C][C]0.0917970421034502[/C][/ROW]
[ROW][C]66[/C][C]0.919212439900795[/C][C]0.161575120198411[/C][C]0.0807875600992054[/C][/ROW]
[ROW][C]67[/C][C]0.918423374626801[/C][C]0.163153250746397[/C][C]0.0815766253731985[/C][/ROW]
[ROW][C]68[/C][C]0.898285557316561[/C][C]0.203428885366878[/C][C]0.101714442683439[/C][/ROW]
[ROW][C]69[/C][C]0.91000670078832[/C][C]0.179986598423359[/C][C]0.0899932992116797[/C][/ROW]
[ROW][C]70[/C][C]0.889099009056177[/C][C]0.221801981887647[/C][C]0.110900990943823[/C][/ROW]
[ROW][C]71[/C][C]0.868656798231746[/C][C]0.262686403536507[/C][C]0.131343201768254[/C][/ROW]
[ROW][C]72[/C][C]0.845843415731828[/C][C]0.308313168536345[/C][C]0.154156584268172[/C][/ROW]
[ROW][C]73[/C][C]0.82426933749693[/C][C]0.351461325006139[/C][C]0.175730662503070[/C][/ROW]
[ROW][C]74[/C][C]0.796757597733303[/C][C]0.406484804533393[/C][C]0.203242402266697[/C][/ROW]
[ROW][C]75[/C][C]0.789833411238326[/C][C]0.420333177523347[/C][C]0.210166588761674[/C][/ROW]
[ROW][C]76[/C][C]0.802410046824846[/C][C]0.395179906350308[/C][C]0.197589953175154[/C][/ROW]
[ROW][C]77[/C][C]0.788384621167522[/C][C]0.423230757664955[/C][C]0.211615378832478[/C][/ROW]
[ROW][C]78[/C][C]0.753383931789521[/C][C]0.493232136420958[/C][C]0.246616068210479[/C][/ROW]
[ROW][C]79[/C][C]0.753901481978203[/C][C]0.492197036043595[/C][C]0.246098518021797[/C][/ROW]
[ROW][C]80[/C][C]0.738061747195006[/C][C]0.523876505609989[/C][C]0.261938252804994[/C][/ROW]
[ROW][C]81[/C][C]0.724422180204356[/C][C]0.551155639591287[/C][C]0.275577819795644[/C][/ROW]
[ROW][C]82[/C][C]0.74486933914194[/C][C]0.51026132171612[/C][C]0.25513066085806[/C][/ROW]
[ROW][C]83[/C][C]0.772488421496388[/C][C]0.455023157007225[/C][C]0.227511578503612[/C][/ROW]
[ROW][C]84[/C][C]0.73885237518055[/C][C]0.522295249638899[/C][C]0.261147624819450[/C][/ROW]
[ROW][C]85[/C][C]0.747706606416283[/C][C]0.504586787167433[/C][C]0.252293393583717[/C][/ROW]
[ROW][C]86[/C][C]0.723671336633689[/C][C]0.552657326732622[/C][C]0.276328663366311[/C][/ROW]
[ROW][C]87[/C][C]0.681729909974356[/C][C]0.636540180051287[/C][C]0.318270090025644[/C][/ROW]
[ROW][C]88[/C][C]0.78495549835949[/C][C]0.430089003281021[/C][C]0.215044501640510[/C][/ROW]
[ROW][C]89[/C][C]0.769433838985687[/C][C]0.461132322028627[/C][C]0.230566161014313[/C][/ROW]
[ROW][C]90[/C][C]0.799046546049216[/C][C]0.401906907901568[/C][C]0.200953453950784[/C][/ROW]
[ROW][C]91[/C][C]0.777233585788498[/C][C]0.445532828423004[/C][C]0.222766414211502[/C][/ROW]
[ROW][C]92[/C][C]0.753196435829288[/C][C]0.493607128341424[/C][C]0.246803564170712[/C][/ROW]
[ROW][C]93[/C][C]0.71232585691901[/C][C]0.57534828616198[/C][C]0.28767414308099[/C][/ROW]
[ROW][C]94[/C][C]0.668776669325121[/C][C]0.662446661349759[/C][C]0.331223330674879[/C][/ROW]
[ROW][C]95[/C][C]0.62779186022122[/C][C]0.74441627955756[/C][C]0.37220813977878[/C][/ROW]
[ROW][C]96[/C][C]0.579436122289843[/C][C]0.841127755420313[/C][C]0.420563877710157[/C][/ROW]
[ROW][C]97[/C][C]0.610803191721143[/C][C]0.778393616557714[/C][C]0.389196808278857[/C][/ROW]
[ROW][C]98[/C][C]0.56305988252169[/C][C]0.873880234956621[/C][C]0.436940117478311[/C][/ROW]
[ROW][C]99[/C][C]0.515419726282714[/C][C]0.969160547434573[/C][C]0.484580273717286[/C][/ROW]
[ROW][C]100[/C][C]0.493308076048108[/C][C]0.986616152096215[/C][C]0.506691923951892[/C][/ROW]
[ROW][C]101[/C][C]0.483156204953539[/C][C]0.966312409907078[/C][C]0.516843795046461[/C][/ROW]
[ROW][C]102[/C][C]0.467953348302502[/C][C]0.935906696605003[/C][C]0.532046651697498[/C][/ROW]
[ROW][C]103[/C][C]0.420904224779434[/C][C]0.841808449558869[/C][C]0.579095775220566[/C][/ROW]
[ROW][C]104[/C][C]0.389479119291345[/C][C]0.77895823858269[/C][C]0.610520880708655[/C][/ROW]
[ROW][C]105[/C][C]0.362525352015146[/C][C]0.725050704030293[/C][C]0.637474647984854[/C][/ROW]
[ROW][C]106[/C][C]0.433324250141309[/C][C]0.866648500282618[/C][C]0.566675749858691[/C][/ROW]
[ROW][C]107[/C][C]0.428895674135222[/C][C]0.857791348270444[/C][C]0.571104325864778[/C][/ROW]
[ROW][C]108[/C][C]0.431411429331738[/C][C]0.862822858663476[/C][C]0.568588570668262[/C][/ROW]
[ROW][C]109[/C][C]0.390370071107653[/C][C]0.780740142215306[/C][C]0.609629928892347[/C][/ROW]
[ROW][C]110[/C][C]0.352740732054552[/C][C]0.705481464109105[/C][C]0.647259267945448[/C][/ROW]
[ROW][C]111[/C][C]0.368190926655984[/C][C]0.736381853311967[/C][C]0.631809073344016[/C][/ROW]
[ROW][C]112[/C][C]0.366324938492512[/C][C]0.732649876985024[/C][C]0.633675061507488[/C][/ROW]
[ROW][C]113[/C][C]0.325703879125683[/C][C]0.651407758251366[/C][C]0.674296120874317[/C][/ROW]
[ROW][C]114[/C][C]0.448544038377966[/C][C]0.897088076755932[/C][C]0.551455961622034[/C][/ROW]
[ROW][C]115[/C][C]0.394796671119665[/C][C]0.789593342239331[/C][C]0.605203328880335[/C][/ROW]
[ROW][C]116[/C][C]0.406195153091627[/C][C]0.812390306183255[/C][C]0.593804846908373[/C][/ROW]
[ROW][C]117[/C][C]0.428563770795835[/C][C]0.85712754159167[/C][C]0.571436229204165[/C][/ROW]
[ROW][C]118[/C][C]0.372116719784850[/C][C]0.744233439569699[/C][C]0.62788328021515[/C][/ROW]
[ROW][C]119[/C][C]0.621211216646473[/C][C]0.757577566707055[/C][C]0.378788783353527[/C][/ROW]
[ROW][C]120[/C][C]0.640558863033936[/C][C]0.718882273932127[/C][C]0.359441136966064[/C][/ROW]
[ROW][C]121[/C][C]0.587104062916222[/C][C]0.825791874167555[/C][C]0.412895937083778[/C][/ROW]
[ROW][C]122[/C][C]0.625105106436965[/C][C]0.749789787126069[/C][C]0.374894893563034[/C][/ROW]
[ROW][C]123[/C][C]0.61744444957885[/C][C]0.7651111008423[/C][C]0.38255555042115[/C][/ROW]
[ROW][C]124[/C][C]0.555486926514507[/C][C]0.889026146970985[/C][C]0.444513073485493[/C][/ROW]
[ROW][C]125[/C][C]0.50427776886023[/C][C]0.99144446227954[/C][C]0.49572223113977[/C][/ROW]
[ROW][C]126[/C][C]0.470549917653022[/C][C]0.941099835306044[/C][C]0.529450082346978[/C][/ROW]
[ROW][C]127[/C][C]0.480032512679873[/C][C]0.960065025359746[/C][C]0.519967487320127[/C][/ROW]
[ROW][C]128[/C][C]0.412664922414594[/C][C]0.825329844829187[/C][C]0.587335077585406[/C][/ROW]
[ROW][C]129[/C][C]0.63514123534114[/C][C]0.72971752931772[/C][C]0.36485876465886[/C][/ROW]
[ROW][C]130[/C][C]0.594301002917109[/C][C]0.811397994165782[/C][C]0.405698997082891[/C][/ROW]
[ROW][C]131[/C][C]0.799401464823038[/C][C]0.401197070353924[/C][C]0.200598535176962[/C][/ROW]
[ROW][C]132[/C][C]0.738203627454924[/C][C]0.523592745090153[/C][C]0.261796372545076[/C][/ROW]
[ROW][C]133[/C][C]0.688652100677116[/C][C]0.622695798645767[/C][C]0.311347899322883[/C][/ROW]
[ROW][C]134[/C][C]0.61932478158588[/C][C]0.76135043682824[/C][C]0.38067521841412[/C][/ROW]
[ROW][C]135[/C][C]0.542998598279952[/C][C]0.914002803440097[/C][C]0.457001401720048[/C][/ROW]
[ROW][C]136[/C][C]0.468036793584562[/C][C]0.936073587169125[/C][C]0.531963206415438[/C][/ROW]
[ROW][C]137[/C][C]0.382235539899655[/C][C]0.76447107979931[/C][C]0.617764460100345[/C][/ROW]
[ROW][C]138[/C][C]0.326107184438964[/C][C]0.652214368877927[/C][C]0.673892815561036[/C][/ROW]
[ROW][C]139[/C][C]0.272257765770569[/C][C]0.544515531541139[/C][C]0.72774223422943[/C][/ROW]
[ROW][C]140[/C][C]0.308337983616509[/C][C]0.616675967233019[/C][C]0.69166201638349[/C][/ROW]
[ROW][C]141[/C][C]0.225118962606646[/C][C]0.450237925213291[/C][C]0.774881037393354[/C][/ROW]
[ROW][C]142[/C][C]0.158919981688500[/C][C]0.317839963377001[/C][C]0.8410800183115[/C][/ROW]
[ROW][C]143[/C][C]0.116192285727414[/C][C]0.232384571454828[/C][C]0.883807714272586[/C][/ROW]
[ROW][C]144[/C][C]0.0679527430220946[/C][C]0.135905486044189[/C][C]0.932047256977905[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98824&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98824&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9910899284354470.01782014312910540.00891007156455268
160.9789930761537670.04201384769246570.0210069238462329
170.9582323405019660.08353531899606850.0417676594980342
180.9284799737602770.1430400524794460.071520026239723
190.9219148945979650.1561702108040700.0780851054020348
200.9408727456067960.1182545087864080.0591272543932041
210.9676516166485740.06469676670285130.0323483833514256
220.9541071228191720.0917857543616570.0458928771808285
230.9315329853877780.1369340292244440.0684670146122221
240.9008286886933320.1983426226133360.0991713113066681
250.8666276132730070.2667447734539850.133372386726993
260.8645066872324540.2709866255350920.135493312767546
270.8216655475927470.3566689048145070.178334452407253
280.7817841672645940.4364316654708120.218215832735406
290.8069567644268970.3860864711462070.193043235573103
300.7645940335754440.4708119328491110.235405966424556
310.7150036960600880.5699926078798250.284996303939912
320.665223359444430.6695532811111390.334776640555569
330.6142927271582520.7714145456834960.385707272841748
340.5506093870421390.8987812259157230.449390612957861
350.52831696894550.9433660621090.4716830310545
360.6254470780098280.7491058439803430.374552921990172
370.5643906437476070.8712187125047860.435609356252393
380.5088395570037480.9823208859925050.491160442996252
390.4669482941092810.9338965882185610.53305170589072
400.4274238804641960.8548477609283920.572576119535804
410.3908752351400140.781750470280030.609124764859986
420.3369580030707020.6739160061414040.663041996929298
430.2865077861800040.5730155723600080.713492213819996
440.2519517143830370.5039034287660730.748048285616963
450.3904741840758620.7809483681517240.609525815924138
460.3386030675866090.6772061351732190.66139693241339
470.3304018060441870.6608036120883740.669598193955813
480.3202807385788840.6405614771577680.679719261421116
490.2742916240703540.5485832481407070.725708375929646
500.2369299778135010.4738599556270030.763070022186499
510.2178286772160260.4356573544320510.782171322783974
520.2285800481113170.4571600962226340.771419951888683
530.2257612863858170.4515225727716330.774238713614184
540.1873304454767870.3746608909535730.812669554523213
550.1618906902962480.3237813805924950.838109309703752
560.1358184491857770.2716368983715550.864181550814223
570.1156895631441210.2313791262882420.884310436855879
580.09954429521469080.1990885904293820.90045570478531
590.08452767930802990.1690553586160600.91547232069197
600.06613968610465870.1322793722093170.933860313895341
610.08447735044225120.1689547008845020.915522649557749
620.2537801870533520.5075603741067030.746219812946648
630.702129226138420.5957415477231590.297870773861580
640.9239344970264530.1521310059470930.0760655029735466
650.908202957896550.1835940842069000.0917970421034502
660.9192124399007950.1615751201984110.0807875600992054
670.9184233746268010.1631532507463970.0815766253731985
680.8982855573165610.2034288853668780.101714442683439
690.910006700788320.1799865984233590.0899932992116797
700.8890990090561770.2218019818876470.110900990943823
710.8686567982317460.2626864035365070.131343201768254
720.8458434157318280.3083131685363450.154156584268172
730.824269337496930.3514613250061390.175730662503070
740.7967575977333030.4064848045333930.203242402266697
750.7898334112383260.4203331775233470.210166588761674
760.8024100468248460.3951799063503080.197589953175154
770.7883846211675220.4232307576649550.211615378832478
780.7533839317895210.4932321364209580.246616068210479
790.7539014819782030.4921970360435950.246098518021797
800.7380617471950060.5238765056099890.261938252804994
810.7244221802043560.5511556395912870.275577819795644
820.744869339141940.510261321716120.25513066085806
830.7724884214963880.4550231570072250.227511578503612
840.738852375180550.5222952496388990.261147624819450
850.7477066064162830.5045867871674330.252293393583717
860.7236713366336890.5526573267326220.276328663366311
870.6817299099743560.6365401800512870.318270090025644
880.784955498359490.4300890032810210.215044501640510
890.7694338389856870.4611323220286270.230566161014313
900.7990465460492160.4019069079015680.200953453950784
910.7772335857884980.4455328284230040.222766414211502
920.7531964358292880.4936071283414240.246803564170712
930.712325856919010.575348286161980.28767414308099
940.6687766693251210.6624466613497590.331223330674879
950.627791860221220.744416279557560.37220813977878
960.5794361222898430.8411277554203130.420563877710157
970.6108031917211430.7783936165577140.389196808278857
980.563059882521690.8738802349566210.436940117478311
990.5154197262827140.9691605474345730.484580273717286
1000.4933080760481080.9866161520962150.506691923951892
1010.4831562049535390.9663124099070780.516843795046461
1020.4679533483025020.9359066966050030.532046651697498
1030.4209042247794340.8418084495588690.579095775220566
1040.3894791192913450.778958238582690.610520880708655
1050.3625253520151460.7250507040302930.637474647984854
1060.4333242501413090.8666485002826180.566675749858691
1070.4288956741352220.8577913482704440.571104325864778
1080.4314114293317380.8628228586634760.568588570668262
1090.3903700711076530.7807401422153060.609629928892347
1100.3527407320545520.7054814641091050.647259267945448
1110.3681909266559840.7363818533119670.631809073344016
1120.3663249384925120.7326498769850240.633675061507488
1130.3257038791256830.6514077582513660.674296120874317
1140.4485440383779660.8970880767559320.551455961622034
1150.3947966711196650.7895933422393310.605203328880335
1160.4061951530916270.8123903061832550.593804846908373
1170.4285637707958350.857127541591670.571436229204165
1180.3721167197848500.7442334395696990.62788328021515
1190.6212112166464730.7575775667070550.378788783353527
1200.6405588630339360.7188822739321270.359441136966064
1210.5871040629162220.8257918741675550.412895937083778
1220.6251051064369650.7497897871260690.374894893563034
1230.617444449578850.76511110084230.38255555042115
1240.5554869265145070.8890261469709850.444513073485493
1250.504277768860230.991444462279540.49572223113977
1260.4705499176530220.9410998353060440.529450082346978
1270.4800325126798730.9600650253597460.519967487320127
1280.4126649224145940.8253298448291870.587335077585406
1290.635141235341140.729717529317720.36485876465886
1300.5943010029171090.8113979941657820.405698997082891
1310.7994014648230380.4011970703539240.200598535176962
1320.7382036274549240.5235927450901530.261796372545076
1330.6886521006771160.6226957986457670.311347899322883
1340.619324781585880.761350436828240.38067521841412
1350.5429985982799520.9140028034400970.457001401720048
1360.4680367935845620.9360735871691250.531963206415438
1370.3822355398996550.764471079799310.617764460100345
1380.3261071844389640.6522143688779270.673892815561036
1390.2722577657705690.5445155315411390.72774223422943
1400.3083379836165090.6166759672330190.69166201638349
1410.2251189626066460.4502379252132910.774881037393354
1420.1589199816885000.3178399633770010.8410800183115
1430.1161922857274140.2323845714548280.883807714272586
1440.06795274302209460.1359054860441890.932047256977905







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0153846153846154OK
10% type I error level50.0384615384615385OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 2 & 0.0153846153846154 & OK \tabularnewline
10% type I error level & 5 & 0.0384615384615385 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98824&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0153846153846154[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.0384615384615385[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98824&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98824&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0153846153846154OK
10% type I error level50.0384615384615385OK



Parameters (Session):
par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}